Unified generator of intelligent tutoring

ABSTRACT

The invention accelerates successful learning in a wide variety of existing and developing learning environments by generating the most effective dynamic adaptive tutoring tailored to a current learner model. It provides a full coverage of a basic tutoring functionality including passive and active tutoring manners, as well as presenting, testing and diagnosing modes. An innovative component of the invention, a unified generator of intelligent tutoring, deals exclusively with a logical aspect of tutoring leaving all media aspects to be realized by traditional components of tutoring systems. The generator represents a generic logical core (brain) of known specific intelligent tutoring systems comprising a reusable tutoring engine and a reusable tutoring knowledge/data framework including a reusable learner model. All together they transform traditionally sophisticated courseware authoring into a simple fill-in-frameworks routine and automatically generate intelligent tutoring in any specific learning environments including available educational, training, simulation, knowledge management and job support systems.

CROSS REFERENCE TO RELATED APPLICATIONS

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STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

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BACKGROUND OF THE INVENTION

The invention belongs to the field of instructional technology foreducation and training as well as to other closely related fields suchas knowledge management, performance support and job aids, coveringcomputer/web-based education and training, so named e-learning, learningmanagement, learning content management, competency-based learning,adaptive model-based learning, and specifically focused on a generativecore of intelligent tutoring systems.

Our theoretical analysis shows that educational and trainingtechnologies (usually presented in very different forms: from e-books,simulators, games, computer/web-based training courses, up tointelligent tutoring systems) include a nesting hierarchy of the samemodels (though some of them exists in embryo or hidden form):

-   -   a) a domain model representing a piece of the world under        learner study. It can be represented in any media form (text,        picture, audio, video, animation, simulation, virtual reality,        physical models and even real objects). The domain model        represents what is given to the learner for study. It supplies        the learner with what to learn and thus represents a supplying        kind of learning resources: presentations, demonstrations,        simulations, and exercises.    -   b) a task model representing job(s), mission(s), task(s) to        perform or question(s) to answer in said domain. The task model        represents not only what is given in the domain, but also what        is required. What is given is already represented with said        domain model. What is required can be assigned to the learner by        a tutor with a message in any media form. In other words, the        task model is a problem situation in the domain to initiate a        specific (problem solving) activity of the learner. It can exist        in a form of exercising, testing and diagnosing learning        resources.    -   c) an expert model representing said job(s), mission(s), task(s)        performing or question(s) answering expertise, procedure and/or        results of a human expert in said domain. In its simplest        embodiment, it can be just an alternative of correct answer in a        multiple choice question. In the most complex embodiment, it can        be an expert system solving certain set of problems in said        domain. In general, an expert model represents a        goal/objective(s) of learning/tutoring process. Additionally, it        can be used as a supplying kind of learning resource to        demonstrate correct solutions to the learner.    -   d) a learner model representing the same job(s), mission(s),        and/or task(s) performing expertise, procedure and/or results of        a particular learner in said domain. It describes said expert        model together with typical deviations of the learner from it.        Such deviations can be used by a tutor additionally as a        supplying learning resource to demonstrate typical incorrect        solutions to the learner.    -   e) a learning space model combining a plurality of instances of        learner models in different time points and for different        learners from a target audience and representing their job(s),        mission(s), and/or task(s) performing expertise, procedures        and/or results in the same domain. It describes learning        goal/objective(s) together with all possible deviations of        learners. In the simplest form, a learning space model can be        represented just as a list of learning cases. If the cases are        mutually exclusive, then it is so named “OR” state space model,        which is simple in theory, but is too large in practice. In        practice, much more compact and affordable is “AND-OR” space        model, which can use a few non-exclusive variables (AND) and        their exclusive values (OR), to represent an enormous plurality        of different learner model cases.    -   f) a tutoring task model representing job/tasks of a tutor in        said learning space. In this task, what may be given is a        learner's position in the learning space and available learning        activities/resources able to change this position; what is        required is an expert's position in said learning space.        Actually, this is a control task of the control theory. As a        rule, a real position of a learner in the learning space is        unknown. So, an observation task is arising. In the observation        task, what is given is a learner, learning space model and        learning activities/resources of testing/diagnosing kind; what        is required is to find learner's position in said learning        space. In said “AND-OR” and “OR” learning space models,        representation of said control and observation tasks are        different. Particularly in the most compact “AND-OR” space        model, the observation task consists of a testing task (to check        achievement of goal/objectives) and diagnosing tasks (to        backtrack faults down to their causes).    -   g) a tutoring expert model (or a tutor model for short)        representing tutoring job/task(s) performing expertise,        procedure and results of an expert tutor activity in said        learning space. In “OR” learning space model, an adaptive        tutoring activity can be represented by twofold. The first, the        tutor observes a learning activity of the learner by using        testing/diagnosing resources trying to find learner's current        position in said learning space. The second, after the position        is found and it is not an expert position, the tutor is able to        precisely select and supply the learner with the best learning        resources for this particular learner trying to “push” him/her        by the most effective way in direction to the expert's position        in this learning space. Then the tutor observes again to define        an updated learner's position for the next best “push” and so        on. In said, more compact, “AND-OR” learning space, the same        process looks threefold, like an integration of supplying,        testing, and diagnosing task solving activities. In reality,        there is no strict separation of supplying, testing, and        diagnosing resources. From one side, testing/diagnosing        resources can cause a change of learner's position in the        learning space. From another side, learner's response on        supplying learning resources can provide certain evidence about        his/her current position in the learning space. That is why in        an ideal case, an expert-tutor should solve said control        (supplying) and observation (testing and diagnosing) tasks in        parallel by intelligent managing all available learning        resources in order to achieve learning goal/objectives by the        most effective way.

The first three (a-c) models are basic and elaborated pretty well ininstructional system design, related generic theories and technologies.See for example (Anderson et al., 1995), (Scandura, 2003). In contrast,the last four (d-g) models are not developed so well so far. Indeed, dueto its nesting structure and incrementing complexity, each next model ismore complex and less developed than previous one. And the leastdeveloped is the tutor model.

Known learner models instantiating said learning spaces are different.The most advanced of them are as follows:

-   -   a) Overlay learner model representing a learner expertise in        terms of what the learner knows and does not know in a specific        domain. See for example,        http://www.cs.mdx.ac.uk/staffpages/serengul/Overlay.student.models.htm.    -   b) Learner model as an expert solution of a specific task as in        model tracing tutors (Anderson et al., 1995);    -   c) Perturbation learner models representing expert systems with        intentionally embedded bugs or just bug libraries collecting        learners' misunderstanding, false concepts, wrong rules, et        cetera. See for example,        http://www.cs.mdx.ac.tuk/staffpages/serenigul/perturbation.student.models.htm.

Fuzzy (Goodkovsky, 1992), Bayesian (Mislevy and Gitomer, 1996), andbelief (Murray and VanLehn, 2000) networks representing variety oflearner models with uncertain assessments and dependencies, which arecommon in tutoring practice.

Known learning space models include said OR and AND-OR space models.Pure OR space model is illustrated with known “knowledge space theory”(Dietrich Albert Cord Hockemeyer, 1997) and a classical Bayesian model.They are not compact and affordable in practice. AND-OR space model isillustrated with simple, affordable and widely spread overlay learnermodels.

Known tutoring job/tasks representation, which actually represents anassignment to fill the gap between an expert and learner models in saidlearning space, is quite different in available theories, technologies,and learning applications. Only commonly recognized tutoring tasks are aplan design, sequencing of learning activities/resources and assessmentsof different kind. Actually, core tasks of any human complex activitycomprise the similar tasks:

-   -   a) Planning,    -   b) Implementation,    -   c) Assessment of progress,    -   d) Assessment-based re-planning.

The tutoring expert model (a tutor model), which should be able to fillthe gap between the expert and learner models in said learning space bysolving above mentioned 1-4 tutoring tasks, is understood andrepresented quite different as well. Perhaps, the most common isunanimous recognition of complexity of a complete tutor model. Anothercommon feature is a prevailing of approach/domain/task-specificheuristic tutors, which are not reusable for other approaches, domainand tasks. See for example (R. Stottller and N. Harmon, 2003). The thirdis a triviality of known reusable technological tutoring solutions. Forexample, existing “high-end” Computer-Based Training authoring toolssupport only simplest manual script/flowchart-based models of tutoringactivity, which in practice is used mostly for linear sequencing of thesame learning activities/resources for all learners. Even AdvancedDistributed Learning Lab's Sharable Content Object Reference Model,SCORM 2004, supports only simple sequencing as well. See(http://www.adinet.org/index.cfm?fuseaction=scormabt).

The known endeavors in generic planning of tutoring activity (fromscratch to the end) are based on implementation of ArtificialIntelligence, which appears to be very sophisticated for commonpractical application (Bruce Mills, 2002). Moreover, due tounpredictability of learning activity, detailed plans developed inadvance (from scratch to the end) are getting obsolete very soon andrequire re-planning after each assessment of real learning progress.

What is really required in tutoring technologies is dynamic adaptiveplanning of learning activity that departs from a current learningprogress (learner's position in said learning space). The problem isthat said current learning progress is directly unobservable and shouldbe indirectly assessed and reassessed in real tine. To be effective andefficient such assessment in its turn requires dynamic adaptive planningas well. There are no yet tools for automating such a complex tutoringactivity. That is why in practice, the automated tutoring is narrowed tovery specific tasks, like in (Liegle; El-Sheikh), or to pre-sequencingof entire learning lessons in contrast to sequencing of fine learningactivities/resources within each lesson, like in (Sun-Teck Tan, 1996).

The most of known intelligent tutoring systems are developed byheuristic-based programming from scratch. As a rule they represent aunique monolith of hardwired learning resources, tools, andassessment/decision makers based on a specific learningtheory/paradigm/vision. See for example (R. Stottler and N. Harmon,2003). As a rule, they are not reusable for other theories andapplications. Though, implementing object-oriented programming paradigmallows developers to accumulate proprietary building blocks toaccelerate building new ITSs, there is no any evidence of any genericblock, which dynamically solves all above mentioned control, observationand diagnosing tutoring tasks for all specific domain applications.

Known Bayesian, fuzzy, belief networks are known to be the finestgeneric tools for dynamic assessment of learning progress, but they areonly the tools that again require programming, which can be done bydifferent way by different developers with their different experienceand visions. Moreover, these networks do not perform required planningfunctions, which are the most critical in intelligent tutoring (Mislevyand Gitomer, 1996).

Known extensions of belief networks with decision making nodes are ablepotentially to support simple planning operations. In (R. Murray andKurt VanLehn, 2000), a belief/decision network has been used to automatea “coaching” task of tutoring activity. Indeed, these belief/decisionnetworks represent a powerful tool for developing intelligentinstructional applications. But again they are just tools, which requiresophisticated reprogramming for each specific domain application.

Known machine learning techniques (e.g., neural networks, case-basedreasoning) are able to replace inevitably complex programming withmachine learning of tutoring activity demonstrated by expert-tutor, butwithout prior tutoring knowledge it requires unrealistically longtraining procedures for really intelligent tutoring.

So, it looks like there are some intractable problems in instructionaltechnologies, which include the following:

-   -   a) no generic compact model of a learning space, specific enough        to represent fine tutoring knowledge/data within any        instructional unit, compliant with known pedagogical theories        and best practices and ready to be used for any new specific        domain and job/tasks to learn;    -   b) no generic model of a learner compliant with the generic        learning space model and specific enough to be easily tuned for        any learner from the target audience;    -   c) no generic model of entire tutoring job/mission specific        enough to represent an integration of tutoring control and        observation tasks, where latter includes testing and diagnosing        tasks;    -   d) no generic model of a tutoring task solver (a tutoring        engine) capable of dynamic adaptive planning and execution of        the multitask tutoring activity in user customized manners and        forms;

Despite of the facts that some solutions of said a-b problems are known,and there are always possibility to dispute solution of said c-dproblems, definitely there is no any consistent solution of all thesea-d problems yet.

In my past work [Goodkovsky 2002], I developed a composition and methodsof computer-based intelligent tutoring system covering a reusablegeneric domain shell and player, tutor model and domain-tutor interface.Particularly, developed technical solution for the tutor modelrepresents a computer program only. This program includes a mix ofgeneric logic and specific media components. It is based on the fuzzylogic and focused mostly on the active tutoring manner, specifically ondynamic adaptive selecting only the next single tutoring assignment.Proposed tutoring task structure is pretty sophisticated and includesfive tasks and three sub-tasks (named as modes and sub-modes). It doesnot separate logic and media of tutoring systems completely. It does notinclude a complete technical solution of passive tutoring. It does notinclude a technical solution of a multiple tutoring assignment oflearning resources for the learner's own choice of single one. Learningresources are entirely separated in two categories—presentations andtests—each with quite different processing. These features makerepresentation of tutoring knowledge/data as well as their processingexcessively complex. There was not invented extensive pre-processing oftutoring data, which could accelerate processing in real time.

Actually, I authored only the provisional patent and did participate inthe nonprovisional patent application [Goodkovsky 2002]. As a result,the nonprovisional patent application was not properly completed.Particularly, it did not disclose the diagnosing procedure in sufficientdetail. Moreover a key component of the system, the reviser of thelearner model, was not disclosed at all. Without the reviser the wholesystem cannot be made and used. These deficiencies eliminate anypossibility to make and use described system by anybody else but me.

So, the main disadvantages of the prior art are as follows:

-   -   a) Uniqueness, low reusability, complexity, and high cost of new        learning applications design;    -   b) Deficiencies in fundamental tutoring functionality, which        eliminate a possibility to accelerate successful learning.

A goal of present invention is to solve above mentioned problems a-drepresenting a core of the instructional technology and intelligenttutoring. Here I developed a new combination of mutually consistentsolutions of these problems. The whole system is not necessarily acomputer-based program. Particularly, it can include any other kind oflearning environment such as physical models, real job tools andequipment. The invention separates the logic and media of tutoringcompletely. It provides generic logical frameworks for tutoringknowledge/data and the generic engine for automatic generating ofintelligent tutoring. A core technical solution represents a unified yetcustomizable generator of intelligent tutoring, which is capable ofsolving a complete set of fundamental tutoring tasks in both passive andactive tutoring manner. In both active and passive manners of tutoring,it provides a dynamic fine assessment of learner's progress withcorresponding tutoring feedback. The active manner of tutoring isrealized with only three fundamental tutoring tasks, named modes(supply, testing and diagnosing). It also realizes multiple tutoringassignments by dynamic adaptive restricting of learner's access toavailable learning activities/resources. Learning resources ofpresentation and test categories are represented uniformly, whichallowed unification and simplification of their processing. Thistutoring generator does not require reprogramming for any newapplication, just entering new application-specific knowledge/data isenough.

Finally, invented methods and compositions are completely describedhereinafter in sufficient detail. So any specialist with regularqualification can make and everybody will be able to use them.

BRIEF SUMMARY OF THE INVENTION

The invention is a method and a system powered by a generator of dynamicadaptive (intelligent) tutoring of a learner in a learning environment.Its goal is to accelerate learning experience by fine monitoring andeffective controlling a learning activity. It is known fact thatintelligent tutoring is able to provide two sigma shift in averagemastery compared with unsupervised learning (Bloom, 1984), which means98% of learning success in average.

The invention realizes the fundamental idea to completely separate logicand media in the learning/tutoring process in order to generalize thelogic and reuse it with any specific media, which can include but is notlimited to traditional learning materials, computer-based media,audio/video players, physical models and real objects under study aswell as their any combination.

The core component of invention, a logic generator of intelligenttutoring, includes a uniform framework-based knowledge/data model,including a learner model, and uniform tutoring engine. It can be usedas a middleware between an administrative layer and contentauthoring/delivering layer of existing and future instructional systems,e-learning, knowledge management, job aid and performance supportsystems.

In an authoring stage, instructional designers do not need anymore tomanually design very sophisticated rules, scripts, or flowcharts oftutoring from scratch. All they need is to fill in said uniformknowledge/data framework with their specific knowledge/data andassociate them with specific (available or to be developed) mediaresources. It significantly simplifies very labor-consuming authoringjob, prevents frequent errors and as a result guarantees a betterquality of a courseware. Due to these features, a requirement bar toinstructional expertise of authors can be lowered and practicallyeverybody can be a successful author of the intelligent courseware. So,the same people can be learners and authors. It opens new horizons for areliable transfer of knowledge/skills among people vs regular veryunreliable transfer of information among them.

In a passive (non-intrusive) manner, that is most appropriate for ajob/performance support and final stages of training, the generatorobtains learning activity reports from a monitor tracking learningactivity of the learner in the learning environment, interprets saidreports, assesses current progress of the learner, optionally providessound assessment-based (vs traditional shallow, tracked data-based only)feedback messages to the learner, and makes main tutoring decisions.Particularly, if identified faults of the learner exceed a predefinedtolerance level or the faults' cause (which is a dead-end of learningprocess) is clearly diagnosed, then it recommends a learner to switch tothe active tutoring manner.

In an active (interventional) manner, that is the most appropriate forconceptual education, initial stages of training, and fault remediation,the tutoring generator extends its passive functionality. It dynamicallyselects a current tutoring mode (supply, testing or diagnosing). Witheach of these modes depending of the learner choice, it can dynamicallyand adaptively pre-select available extra learning activities/resourcesfor a final choice of the learner, rate available learningactivities/resources in accordance with their current personal utilityfor informed learner's choice, or automatically select the best nextlearning activity/resource. All of these are performed to achievedesired learning objectives by the most effective way tailored to apersonal learner's style preferences and current assessment of learningprogress through the learning objectives.

The learning environment can be quite different. Its main mission in thetutoring system is to physically support desired learning activity ofthe learner by creating specific learning situations and getting backlearner's response. The learning environment can include any real objectfor study or its more transparent, cheaper, non-dangerous physicalreplica. It can be a real job/mission environment: an equipment tomaintain, truck to drive, telephone to communicate, computer to operateet cetera. In particular computer-based embodiment, the learningenvironment can include multimedia (text, audio, graphic, video,animation, simulation, game, and virtual reality) and providepre-storing, retrieval, delivery and playing back available learningresources (presentations, simulations, exercises, and tests). The onlylimit for using any available environment as a learning media is ourability to enable monitoring and controlling of the learning activity init. But this ability is defined with other parts of the tutoring system,a logic-media converter, which includes a monitor and a controller.

In general, the monitor performs:

-   -   a) tracking an actual learning behavior including tutoring        assignment (i), learning situation and a corresponding learner's        actual response;    -   b) pre-storing expected responses {k} of a learner (an        expert-like response, at least) in typical learning situations        {s} within tutoring assignment (i);    -   c) identifying an actual behavior of the learner including        selected assignments, learning situations and responses by        comparison their actual tracked data with corresponding        pre-stored data;    -   d) providing the generator with behavior reports including        identifiers of selected assignment (i′), recognized situation        (s′) and learner's response (k′).

Specific embodiments of the monitor depend of specific embodiment of thelearning environment and are well known in instructional technologies.

In general, the controller performs:

-   -   a) accepting tutoring decisions from the logic generator;    -   b) generating commands on the learning environment to execute        tutoring decisions.

Specific embodiments of the controller also depend of specificembodiment of the learning environment and are well known ininstructional technologies.

The logic generator is the most innovative component of the wholesystem. It deals exclusively with logical data by:

-   -   a) making main tutoring decisions including decisions to        -   1. to end tutoring and provide tutoring report to the            administrator,        -   2. switch current passive manner to the active manner of            operation,        -   3. set up a current tutoring mode (supply, testing or            diagnosing),        -   4. pre-select available learning activities/resources for            the learner's own choice,        -   5. rate pre-selected learning activities/resources for            learner's informed choice,        -   6. directly assign specific learning situations for the            learner to initiate his/her desired learning activity,        -   7. decisions to provide commenting and feedback messages,    -   b) providing the controller with said decisions for executing in        the media environment;    -   c) letting the learner to realize assigned learning activity in        the media environment;    -   d) accepting the learning report from the monitor;    -   e) interpreting each accepted report into internal generator's        knowledge/data and    -   f) adapting generator's current knowledge/data about current        learning state of the learner.

In preferred extended embodiment, the whole system includes also anauthoring tool to support logical part of courseware creation. This toolis based on a set of tutoring knowledge/data frameworks and can beintegrated with existing multimedia, CBT, and simulation authoring toolsin order to:

-   -   a) combine logical design and media development in the most        consistent way;    -   b) provide logical skeletons (blue prints) for design of new        media flesh;    -   c) reveal logical skeletons behind available media flesh;    -   d) check mutual logical consistency and sufficiency of        courseware;    -   e) test and debug the created logic on an early logical stage        prior to investing in any media design and development.

In terms of getting popular Advanced Distributed Learning and SharableContent Object Reference Model (SCORM), the invention provides existingand perspective learning (content) management systems, which automatesmainly administrative functions, with the following pure tutoringextensions:

-   -   a) Uniform logical frame work for specification of intelligent        Shareable Content Objects to extend the regular Shareable        Content Object framework;    -   b) Uniform sequencing engine for a tutoring run-time environment        able to dynamically and adaptively sequence Sharable Content        Assets in said intelligent Shareable Content Objects to extend        available engines for simple sequencing: free browsing, linear,        branching, etc.;    -   c) Uniform communication protocol between said intelligent        Shareable Content Objects and said uniform sequencing engine.

The most important feature of the invented technical solution is itsreusability or uniformity. The reusability or uniformity is due to thefollowing reasons:

-   -   a) No restrictions on a domain, job/mission/task, or activity to        learn.    -   b) No restriction on learning media environment.    -   c) Separation of generalizable logic (skeletons) from specific        media (flesh) and dealing exclusively with generalizable logic,        leaving all specific media data and operations for the learning        environment and the logic-media converter.    -   d) generic logical representation of tutoring process as an        objective-oriented control over an ill-observable and        ill-controllable object (a learner) by sequencing available        control (learning supply) and observation (testing/diagnosing)        resources;    -   e) separation of domain/tasks-specific tutoring knowledge/data        and generic domain/tasks-independent tutoring engine, which uses        this knowledge/data;    -   f) providing a generic framework for said domain/tasks-specific        tutoring knowledge/data.    -   g) use of very generic conception of learning objectives as a        uniform basis to define different kind of targeted experiences,        abilities, knowledge, skills, attitudes, which can be domain,        tasks and activity specific;    -   h) combining traditionally separated known approaches to        intelligent tutoring systems design on one logical basis        including model tracing tutors (Anderson et al, 1995), adaptive        hypermedia (Brusilovsky, 2003), belief/decision networks        (Murray, 2000) etc;    -   i) using the same logical framework for specification of all        kind of specific learning resources including presentations,        simulations, exercises, tasks and questions;    -   j) using a uniform framework for representing specific personal        data of any learner;    -   k) matching even uncertain specific knowledge/data into its        generic formal frameworks.

The other important feature of the invention is its functionalcompleteness which is due to:

-   -   a) Realization of passive and active manners of tutoring;    -   b) Realization of basic supply, testing and diagnosing modes ill        the active tutoring manner;    -   c) Realization of strategic, tactic and operative tutoring        decisions in each tutoring mode;    -   d) Wide scale customization of decision making based upon a        plurality of variable parameters of strategic, tactic and        operative decisions;    -   e) Mixed initiative control over learning by        -   1. Generator's restriction of learner's access to available            learning activities/resources for his/her personal choice,        -   2. Generator's rating of learning activities/resources for            informed choice by the learner or        -   3. Generator's direct assignment of single learning resource            to learn;    -   f) Wide scale dynamic personal adaptation particularly including        a personal testing delay, difficulty limit, media features, and        selection of learning resources.

So, the main advantages of the invention are as follows:

-   -   a) Uniformity, high reusability, simplicity, and low cost of new        learning applications design;    -   b) Completeness of fundamental tutoring functionality, which        provides a necessary basis for accelerating successful leaning.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

FIG. 1 is a conceptual diagram which illustrates a generic environmentof the invention.

FIG. 2 is a conceptual diagram of the method of tutoring

FIG. 3 is a conceptual diagram of providing the media environment

FIG. 4 is a conceptual diagram of providing the tutoring logic generator

FIG. 5 is a conceptual diagram of providing the media-logic converter

FIG. 6 is a conceptual diagram of associating the logic generator andthe media environment with the logic-media converter

FIG. 7 is a conceptual diagram of the general tutoring method

FIG. 8 illustrates an external functionality of the tutoring system

FIG. 9 illustrates a generic composition of the tutoring system

FIG. 10 illustrates an example of multi-channel tutoring communication

FIG. 11 is a flowchart of tutoring system operating

FIG. 12 illustrates composition of the learning media environment

FIG. 13 is a flowchart of general operating the learning mediaenvironment

FIG. 14 is a composition of the media-logic converter

FIG. 15. is a flowchart of general operating of the controller

FIG. 16. is a flowchart of general operating of the monitor

FIG. 17. is a flowchart of tutoring system operating in passive manner(case 1)

FIG. 18. is a flowchart of tutoring system operating in active manner(case 2)

FIG. 19. is a flowchart of tutoring system operating in active manner(case 3)

FIG. 20 is a flowchart of tutoring system operating in active manner(case 4)

FIG. 21 illustrates composition of the tutoring logic generator

FIG. 22 illustrates a flowchart of the tutoring generator operating

FIG. 23 illustrates a composition of the knowledge/data model

FIG. 24 illustrates composition of the learning space framework

FIG. 25 illustrates a state transition diagram of a single learningobjective

FIG. 26 is a table representation of prerequisite relations

FIG. 27 is a sample of network representation of the state space model

FIG. 28 is a tree representation of the state space framework

FIG. 29 is a table representation of the behavior space framework

FIG. 30 is a sample of table representation of single tutoringassignments

FIG. 31 is a table representation of the state-behavior relation

FIG. 32 is a table representation of learner's requirements as acheck-list

FIG. 33 is a table representation of learner's preferences as acheck-list

FIG. 34 is a table representation of the learner state framework/model

FIG. 35 is an example of network representation of the learner statemodel

FIG. 36 is a tree representation of the tutoring knowledge/dataframework. Part A.

FIG. 37 is a tree representation of the tutoring knowledge/dataframework. Part B.

FIG. 38 is a table representation of initial diagnostic data

FIG. 39 is a table representation of pre-processed diagnostic data

FIG. 40 is a composition of the tutoring engine

FIG. 41 is a flowchart of the tutoring engine operating

FIG. 42 is a composition of the decision maker

FIG. 43 is a flowchart of operation of the decision maker

FIG. 44 is a flowchart of the strategic decision maker operating

FIG. 45 is a table representation of strategic decision making

FIG. 46 is a flowchart of tactic decision making

FIG. 47 is a table representation of the tactic decision making

FIG. 48 illustrates an operative decision making flowchart

FIG. 49 is a sharp filtering flowchart

FIG. 50 is an updating flowchart

FIG. 51 is a flowchart of revising.

DETAILED DESCRIPTION OF THE INVENTION

An environment or a super-system of the invention is an education,training, knowledge management, performance support and job aids. It cancomprise an administration, courseware authors, instructors, andlearners as well as certain services, tools and resources. See FIG. 1.

In this context, the main goals of the invention are to:

-   -   a) simplify courseware design by authors;    -   b) automate job of instructors;    -   c) accelerate learning experience of learners;    -   d) enable improving management by administration;    -   e) save labor, time and resources by providing new methods and        tools.

The basic ideas of the invention are

-   -   a) complete separation of logic and media in the tutoring        process,    -   b) rationalization and generalization of the tutoring logic and    -   c) reuse of generalized tutoring logic with any specific media        in authoring and tutoring process.

Wherein:

-   -   a) said logic represents mainly tutoring knowledge/data and        tutoring decision making;    -   b) said media is a physical environment to support the learning        activity of the learner. Examples of the media are paper        materials/books with text and graphics, electronic books,        audio/video, computer-based multimedia interactive        presentations, simulators, virtual reality, physical models of        real objects, and even real objects under study.

The invention is a method, system and generator of dynamic adaptive(intelligent) tutoring of a learner in a wide variety of specificlearning media environments.

The Method.

As illustrated in FIG. 2, an entire method for dynamic adaptive(intelligent) tutoring comprises the following main phases:

-   -   a) Providing 100 a tutoring system including        -   1. providing 101 a media environment for physical supporting            a learning activity of said learner;        -   2. providing 102 a logic generator for making a plurality of            tutoring decisions;        -   3. providing 103 a media-logic converter            -   A. for transforming said tutoring decisions into                commands on said media environment to support said                learning activity of said learner in said media                environment and            -   B. for reporting said learning activity into said logic                generator;        -   4. associating 104 said logic generator with said media            environment by said media-logic converter;    -   b) tutoring 105 the learner with said tutoring system by        controlling over said learning activity of said learner in said        media environment with said logic generator through said        logic-media converter;    -   c) Optional evaluating 106 said tutoring system;    -   d) Optional improving 107 said tutoring system.

Said method completely separates media and logic of the tutoring. Itenables generating a media-specific tutoring process based upongeneralized logic, simplifying authoring, improving quality of saidtutoring process and accelerating learning success;

The phase 101 of providing the media environment includes but notlimited to providing 108 a domain model (or a domain for short) forstudy and providing 109 a tutoring persona, which represents a physicalembodiment of the tutoring logic generator for the learner. See FIG. 3.

Examples of the domain (model) for learner's study can be presented inpaper/electronic books, audio/video clips, computer-based multimediainteractive presentations, animations, simulators, virtual reality,physical models of real objects, and even real objects for study.

Examples of the tutoring persona can be presented with pieces ofinstructional text in traditional paper/electronic textbook, audiodevice for providing a learner with feedback, device providingcommunication (like e-mail), computer-pictured/animated/simulatedpersona, a talking head, a virtual tutor, or even a real human tutor,who follows decisions/advices of the logic generator on what to do next.

Due to provided composition, the learning media environment can supportseveral channels of communication with the learner including commenting,progress display, navigating, control over tutoring et cetera.

In its turn, as shown in FIG. 4, the phase 102 of providing a logicgenerator includes:

-   -   a) providing 110 a knowledge/data model referenced to an        instructional unit, said learner, and said learning activity,        which comprises:        -   1. providing 113 a memory for storing tutoring            knowledge/data;        -   2. providing 114 the memory with a reusable uniform            framework for representing said tutoring knowledge/data;        -   3. providing 115 said reusable uniform framework with said            tutoring knowledge/data;    -   b) providing 111 a reusable uniform tutoring engine for making a        plurality of tutoring decisions based upon said knowledge/data        model, which comprises:        -   1. optional providing 116 a preprocessor for knowledge/data            preprocessing;        -   2. providing 117 a decision maker for making a plurality of            tutoring decisions based upon said knowledge/data model;        -   3. providing 118 a processor for adapting said            knowledge/data model based upon a learning report about said            learning activity of said learner and decisions made by said            decision maker;    -   associating 112 said knowledge/data model with said reusable        tutoring engine.

The phase 103 of providing a media-logic converter includes at leastproviding 120 a controller for executing tutoring decisions in the mediaenvironment and providing 121 a monitor for tracking and reportinglearning activity of the learner. See FIG. 5. Depending of providedlearning media environment, said providing 120 a controller andproviding 121 a monitor can include providing several channels ofmedia-logic converting, for example, for commenting, feedback, progressdisplay, learner's control over tutoring et cetera, where each channelincluding a controller and/or a monitor.

As it depicted in FIG. 6, the phase 104 of associating the logicgenerator and the media environment with the media-logic converterincludes

-   -   a) associating 122 the logic generator with the media-logic        converter to enable tutoring control and communication with the        media environment and    -   b) associating 123 the media-logic converter with the media        environment to support control and monitoring of the media        environment.

The phase 105 of tutoring can take control at any time after step 104.After completion its operation it transfers control to step 106. Thetutoring can represent two nesting loops as shown in FIG. 7.

The internal loop depicted in FIG. 7 with dashed lines generates andrealizes tutoring decisions (such as decisions to comment learningprogress), which are not supposed to change tutoring knowledge/data andincludes:

-   -   a) making 130 tutoring decisions by the decision maker based        upon the knowledge/data model;    -   b) executing 131 said tutoring decisions by the media-logic        converter providing necessary commands on the learning media        environment;    -   c) physical supporting 132 the learning activity of the learner        by the media environment;    -   d) monitoring 133 the learning activity and providing the        decision maker with a report by the media-logic converter;    -   e) making 130 new tutoring decisions by the decision maker based        upon the same knowledge/data model;

The external loop depicted in FIG. 7 with solid lines includes all steps130-133 of the internal loop plus a step of adapting 134 theknowledge/data by a processor based upon the learning report and thedecision made. The adapting step 134 changes the knowledge/data modeland makes a difference in the following decision making 130. Namely thisloop plays a key role in dynamic adaptive tutoring.

The described method provides automatic generating of a dynamic adaptivetutoring process, excludes prior manual design of the tutoring processby authors, improves quality of the tutoring process and accelerateslearning success.

The optional phase 106 of evaluating the tutoring system in the finestdetails can include collecting data about personal progress caused byeach tutoring decision, integrating these data across all learners andproviding an assessment of integral efficiency of each tutoringdecision.

The optional phase 107 of improving the tutoring system can be realizedin manual, automated and automatic forms. In any of these forms itincludes

-   -   a) analysis of learning data;    -   b) incrementing tutoring beliefs (from the knowledge/data model)        used during a successful learning;    -   c) decrementing tutoring beliefs (from the knowledge/data model)        used during a fault learning.

Note that steps 131-133 should be activated in described sequence, butcan be performed in parallel.

The System

Definition:

The tutoring system is provided on the phase 100 of described method andrealizes the tutoring of the learner on the phase 105. See FIG. 2.

Functionality:

The complete tutoring system 140 works with two main categories ofusers: administrators and learners. See FIG. 8.

Working with the administrator, the system accepts administrativeassignments and returns tutoring reports.

Note that adult learners are often allowed to play a role of their ownadministrator. In this case, the learner can navigate through units ofinstruction, define tutoring style (to some degree), see progressreports, et cetera.

Working with the learner, the tutoring system controls over at least onespecific leaning activity of the learner by

-   -   a) commenting current progress of the learner with a set of        messages {c},    -   b) creating specific learning situations {t} including controls        in the media environment and    -   c) monitoring the learning activity including learner responses        {k}.

The tutoring system can also provide the learner with a visual displayof current progress, navigation means, specific controls to select atype of tutoring assignments, et cetera.

Parameters:

System's functioning with the learner is defined by the administrativeassignment provided by the administrator.

The administrative assignment includes at least:

-   -   a) an identifier of instructional unit;    -   b) testing threshold as a parameter of learning/tutoring        sufficiency,    -   c) a manner of tutoring (passive or active).        Composition

Actually, the tutoring system 140 has a complex hierarchical structure.But, as illustrated in FIG. 9, its generic composition can be simpleenough and include:

-   -   a) the tutoring logic generator 141 representing a brain of the        tutoring system. It includes tutoring knowledge/data and makes        tutoring decisions;    -   b) the learning media environment 143 representing the domain        under study and the tutoring persona to interact with. It        physically supports at least one learning activity of the        learner by providing him/her with specific learning media,        controls, display, et cetera;    -   c) the media-logic converter 142 coupled with said tutoring        logic generator 141 and said learning media environment 143 for        command/control/communicating said tutoring logic generator 141        with said learning media environment 143.

In more detail, as illustrated in FIG. 10, the tutoring system canincludes a plurality of command/control/communication channels with thelearner, where each channel supports a specific kind of communication.

For example:

-   -   a) channel for learner's performance feedback with (voice)        messages {f};    -   b) channel for commenting a learning progress with messages to        the learner;    -   c) channel for commenting a tutoring manner/mode selection;    -   d) channel for providing a tutoring assignment (i) to realize        learning situation (s) and returning learner's response (k);    -   e) channel for selecting type of tutoring assignments by the        learner;    -   f) channel for displaying current learning progress;    -   g) channel for supporting navigation of the learner through        content;    -   h) channel for a question-answer service;    -   i) channel for help service;    -   j) channel for dictionary, et cetera.

Splitting the whole command/control/communication “pipeline” into thesespecific channels does not change the generic structure of the tutoringsystem (as it is in FIG. 9). Most of these channels are known ininstructional technologies and can be easily realized by an averagespecialist. But not all are always necessary. To provide reasonablecoverage, only the most representative domain/problem-independentchannels will be described hereinafter.

Particularly, as depicted in FIG. 7 and FIG. 10 with dashed lines, theinternal tutoring loop can include the following:

-   -   a) comment channel for providing the learner with tutoring        decision commenting messages {c};    -   b) control channel for learner's control over the tutoring        generator 141 by selecting a manner, style parameters and type        of tutoring assignments;

Due to domain/problem independency these channels can be easily realizedin one uniform embodiment for all possible domains and task/problems. Ingeneral tutoring procedure depicted in FIG. 7, these channels supportsteps 131-133 of the internal loop of tutoring 130-131-132-133-130,which does not change the knowledge/data of the generator.

In contrast, the situation/response channel for providing the tutoringassignment {i}, generating learning situation (s) and returninglearner's response (k) is domain/problem specific. In FIG. 7 and FIG. 10it is illustrated with solid lines. It also supports tutoring steps131-133, but for the external loop of tutoring 130-131-132-133-134-130,where the tutoring knowledge/data of the generator are adapted. Incomparison with comment and control channels, the design of thesituation/response channel is complex and innovative, that is why themost attention will be given to it hereinafter.

Described composition of the tutoring system enables its reuse fordifferent domains and job/tasks and allows saving on an authoring laborand improving the quality of tutoring and learning success.

Operation:

The tutoring system 140 is designed to automatically realize thetutoring phase 105 of the invented method as shown in FIG. 7. In moredetail, the operation of the tutoring system is illustrated in FIG. 11.

Starting said tutoring system can be performed by any user with grantedadministrative rights including an administrator, author, instructor,and the learner;

Being started at any time after the step 104, the system performs thefollowing steps of operation:

-   -   a) Optional accepting 150 the administrative assignment by the        logic generator 141;    -   b) Optional preprocessing 151 knowledge/data model for its        transforming from a storage format to an application format and        system's adjustment according to the administrative assignment.        It is done by the logic generator 141 as well;    -   c) Making 130 tutoring decisions (t) by the logic generator 141        including:        -   1. Making decision to end tutoring; If this is a case, then            the next steps are:            -   A. Commenting this decision;            -   B. Optional providing 152 the tutoring report by the                logic generator 141;            -   C. ending the system operation and            -   D. transferring control to the step 106;        -   2. Making achievement decisions and commenting these            decisions;        -   3. Making manner/mode {m} decisions and commenting these            decisions;        -   4. In active manner and in possible cooperation with the            learner through the control channel, making tutoring            assignment (i′) of the next target situation (s) through the            situation/response channel and commenting this decision;    -   d) In active manner, executing 131 the assignment (i′) of        specific situation (s) in the situation/response channel by        providing commands a(s) on the media environment 143 by        logic-media converter 142 to realize corresponding situation        (s);    -   e) Supporting 132 learning activity of the learner through the        situation/response channel by the learning media environment 143        including        -   1. Generating a current learning situation (s) (under            control from media-logic converter 142 or independently);        -   2. Providing the learner with corresponding media to            materialize the current learning situation (s) with controls            for learner responsive actions. It is done by the learning            media environment 143;        -   3. letting the learner explore provided media and act on            controls, which can provide events (e) and change the            situation (s);    -   f) monitoring 133 learning activity of the learner through the        situation/response channel of the media environment 143 and the        media-logic converter 142; providing the logic generator 141        with the learning report including:        -   1. tutoring assignment (i′);        -   2. all identified situation (s′) and        -   3. an identified response (k′) of the learner on this            situation (s′);    -   g) adapting 134 said knowledge/data model by the logic generator        141;    -   h) making 130 new decisions based upon adapted knowledge/data        model. It is done by the logic generator 141.

Where, said commenting means providing comments {c} through the commentchannel by performing the following steps of the internal loop:

-   -   a) making 130 decision to provide comment (c) by the logic        generator 14;    -   b) executing 131 this decision with media-logic converter 142 by        providing necessary commands a(c) on media environment 143 by        the media-logic converter 142;    -   c) supporting 132 learning activity by comment message delivery        to the learner by media environment 143,    -   d) optional monitoring 133 and capturing delivery confirmation        event (e);    -   e) transferring control back to the decision making 130;

The learner in the tutoring system is provided with opportunity tocontrol over his/her own tutoring through the control channel 131-133 ofthe internal tutoring loop. First the media environment 143 provides 132corresponding controls. Then the learner acts on provided controls ofmedia environment 143 generating special events (e), which are monitoredand identified 133 by the media-logic converter 142 and transferred tothe logic generator for taking into account in making 130 tutoringdecisions.

In FIG. 11, optional components are depicted with dashed lines and thecomment and control channels of the internal loop are illustrated withdashed arrows.

Whereby said system completely separates media and logic of tutoringprocess provides specific media-independent and generalized logic-basedgenerating the tutoring process, simplifies labor-consuming authoring,improves quality of said tutoring process and accelerates learningsuccess.

Learning Media Environment

Definition:

The learning media environment 143 is a part of said tutoring system140. It physically supports learning activity of the learner withinspecific instructional unit providing tangible objects to interact with.The examples of the learning environment 143 are traditional paperbooks, electronic books, computer/web-based presentations, simulators,games, virtual reality, physical models of real objects under study(dummies) and can even include real objects (like a car, engine,dashboard, . . . ).

This part 143 of the tutoring system 140 is not innovative and wasintentionally kept “as is” in majority of traditional tutoring systemsfor enabling maximal reuse of a learning media legacy and lowering acost of a new tutoring systems design. The reason for its considerationhereinafter is a maximal clarification of an operating environment ofthe innovative tutoring generator 141.

Functionality:

In interaction with a learner, the learning media 143 can provide thelearner with

-   -   a) an introduction, specification of objectives and summary of        the instructional unit;    -   b) comment messages {c} including        -   1. achievement commenting messages {v} of the tutoring            generator 141;        -   2. feedback messages {f} of the converter 142 commenting            learner's response;        -   3. manner, mode and assignment decision commenting messages            the tutoring generator 141;    -   c) learning progress display;    -   d) controls for the learner to support a choice of manners, a        kind and an instance of tutoring assignments;    -   e) a set of learning situations {5}, each including controls for        learner's responsive actions;

It accepts learner's control and responsive actions {k} on providedcontrols.

In interaction the with media-logic converter 142, learning mediaenvironment 143 accepts commands {a} and returns events {e} fortracking. By this way, it realizes “If (a), then (e)” function.

Parameters:

In interaction with the learner, the specific functionality of thelearning media 143 is defined with commands from the media-logicconverter 142. This facilitates external control over the learning mediaenvironment 143 by the tutoring logic generator 141.

Functioning of the media environment 143 may depend of other parameterssuch as a resolution, speed, duration, kind of media, et cetera. Thisprovides an extra opportunity for adaptation of the learning mediaenvironment 143.

Composition

As it is illustrated in FIG. 12, the learning media environment 143 cancomprise the following components:

-   -   a) a learning domain (model) 160 represented in a tangible        physical form for exploring/studying by the learner. The domain        supports a situation/response channel of learning communication        by providing a domain aspect (d) of the learning situation (s).        In general, it is optional to have the separate domain model in        the tutoring system.    -   b) a tutoring persona 161 for tangible representing the logic        generator 141 to the learner in all kind of generator-learner        communications. It can provide:        -   1. introduction, objectives and summary presentations;        -   2. comment messages {c} including            -   A. Standard achievement commenting messages {v};            -   B. Standard feedback messages {f} commenting each                learner's response (optional);            -   C. manner, mode and assignment commenting messages;        -   3. learning situations {s} including            -   A. Explanations of the domain;            -   B. Problem posing message;            -   C. Controls to enter learner's solution;        -   4. progress information about current learning state of the            learner;        -   5. control opportunities including a choice of manners, a            kind and an instance of the tutoring assignment.

Said learning domain 160 represents a physical embodiment of what to belearned. It provides a domain aspect (d) of the whole learning situation(s). Even if the “what to be learned” is pure conceptual, like math, ithas to be represented in tangible physical form for the learner tointeract and explore. The learning domain can be a chapter of apaper/electronic book, a loaded audio/video player, computer-basedsimulator/game, physical model of real object and even a real objectitself. The learner should be able to interact and explore the learningdomain by browsing and acting on its controls. The learner can do itindependently or under control of the tutoring generator, the latter ismuch more effective.

The tutoring persona 161 represents a physical embodiment of thetutoring logic generator 141. It can be represented with different mediaas well. The examples of different materialization forms of the tutoringgenerator 141 can include but not limited to certain pieces ofinstructional text in a traditional paper/electronic textbook, audiodevice for feedback providing, device providing communication (e.g.,e-mail), computer-pictured/animated/simulated persona, a talking head, avirtual tutor, or even a real human tutor, which uses the logicgenerator for advising on what to do next and then executes this advisein real tutoring actions.

At a minimum, the learning media environment 143 can include only thetutoring persona 161, which can support all channels of learningcommunications somehow and particularly is able to explain the domain160 under study for the learner. Sometimes it is enough for educationalapplications of the tutoring system. But in training and job-supportapplications of the tutoring system, presence of the domain model israther obligatory.

In traditional learning media 143, the learning domain 160 and tutoringpersona 161 are often not separated in media embodiment and represent amonolith of mixed leaning and tutoring materials. All together theyprovide all necessary functionality described above.

Operation:

Despite of diversity and possible complexity of the learning mediaenvironment 143, on a functional level, its operation seems to besimple.

As shown in FIG. 13, the learning environment takes control from step131 with commands from media-logic converter 142 and includes:

-   -   a) providing 162 the learner with interactive media, which can        include:        -   1. providing introduction, objectives and summary            presentations by the tutoring persona;        -   2. providing comment messages {c} by the tutoring persona            161 including:            -   A. providing achievement commenting messages;            -   B. providing feedback messages {f} of the converter 142                commenting learner's response (optional);            -   C. providing manner, mode and new assignment commenting                messages;        -   3. providing problem (p) posing by the tutoring persona 161;        -   4. providing domain aspect (d) of situation (s) including            controls for learner's actions. It can be done the most            realistically with the domain 160 and/or abstractly by the            tutoring persona 161;        -   5. providing progress display of current learning state of            the learner by the tutoring persona 161;        -   6. providing control opportunities for the learner including            a choice of manners, assignment kind and instance of            assignments by the tutoring persona 161;    -   b) accepting 163 learner's control actions and response (k) by        said controls of the media domain 160 or the tutoring persona        161.

After completion of its operation, it transfers control to step 133 withevents to the media-logic converter 142.

In wide range of all possible learning applications, its mainfunctionality, can be specified in more detail and distributed among itscomponents by different ways.

For example:

-   -   the domain model 160, let say a flight simulator, provides        domain situations {d} with controls for response (k). The        learner is tasked with problem (p) beforehand and knows what is        required to do. The problem (p) completes the domain        situation (d) up to a complete problem situation (s). In this        case, the tutoring persona 161 comments learning progress with        messages {c}. This case is typical for a job support with        passive non-intrusive tutoring.    -   the domain model 160, let it be a flight simulator again,        provides domain situations {d} with controls for response (k).        But the learner is not tasked beforehand. In this case, the        tutoring persona 161 can pose the problem (p) for the learner        creating a complete problem situation (s) and comment learning        progress with messages {c}. This is the case of testing the        learner by posing problems to perform in the domain with        real/media controls.

The domain 160 provides domain situations {d} with no controls forresponse (k). The learner is not tasked beforehand. The tutoring persona161 asks the learner a question (p) creating a problem situation (s) andprovides its own controls for response (k). It can comment the learningprogress with messages {c} as well. This is the case of testing thelearner with presenting the domain, asking questions related to thedomain and getting responses.

There is no separate domain 160 at all. The learner is not taskedbeforehand. The tutoring persona 161 does everything itself: explainsdomain situation (d), pose the problem (p) creating the complete problemsituation (s) for the learner and provides him/her with necessarycontrols for response (k) and then comments learning progress withmessages {c}; This is the typical case of tutoring by communication oftutoring persona with the learner one-one-one.

Embodiments

The tutoring generator 141 is invented to work practically with anylearning media environment 143. Examples of the learning mediaenvironment 143 (comprising the domain model 160 and the tutoringpersona 161) can include, but are not limited to, the followinginstances.

Paper textbook. In a paper textbook, all situations {I} are presentedwith text and pictures on paper pages. Each external command (a) is aspecific page opening. Paper textbook can provide controls (such asmultiple choice for checking, blanks for filling in) and comments {c}for the learner. The learner working with the textbook can generateevents (e), for example by checking alternatives of multiple choices andfilling in the blanks.

Electronic book. In an electronic textbook, all learning situations {I}can be presented with text, graphics, audio, video, animation andsimulation on electronic pages. Each external command (a) opens aspecific electronic page. Electronic textbook can provide a wide varietyof controls (such as multiple choice, fill in the blanks, buttons, hotspots, links, menus, drag and drops, . . . ) and comments {c} for thelearner. The learner can generate events (e), for example by browsing,hitting buttons, clicking, dragging and dropping media objects.

Audio/video player loaded with an audio/video disk. The learningsituations {s} are presented with audio/video playback. Each externalcommand (a) launches a specific track, record. Players can provide somecontrols (such as buttons) and even comments {c} for a user. The learnercan generate events (e), for example, by hitting these buttons.

E-mail. The learning situations {I} and comments {c} can be presentedwith just a text in some cases upgraded with multimedia. Each externalcommand (a) launches a specific message to the learner. Each e-maildevice (cell phone, personal digital assistance or computer) providessome controls (keyboard) for a user/learner, which the learner uses totype in a responsive message (k).

Computer-based interactive presentations. Similar to the electronictextbook, comments {c} and learning situations {s} in a computer can bepresented in a form of interactive presentations including test,graphics, audio, video, animation and simulation. External commands {a}can launch specific interactive presentations for the learner.Interactive presentations can include a wide variety of controls (suchas multiple choice, fill in the blanks, buttons, hot spots, links,menus, drag and drops, . . . ) for the learner. By browsing interactivepresentations and acting on controls, the learner generates events {e}in this learning environment.

Computer-based applications. A majority of computer-based applications(including simulators and games) can be considered as a specificfunctionality mediated for the user with specific interactivepresentations on a computer. Each such an application provides theuser/learner with a variety of situations {s} presented in a form ofwindows/panels with test, graphics and controls. External commands {a}on the application can launch the entire application, its specificmodes, windows, panels, and steps for the user/learner. The applicationcan include a wide variety of controls (such as buttons, links, menus, .. . ) for the learner. Exploring the application by acting on itsdifferent controls and activating its different modes, windows, panels,and steps, the learner generates events {e} in this learningenvironment.

Computer-based training course. Computer-based training courses can beconsidered as specific computer-based applications, which alreadyinclude some tutoring functions. Each such course provides the learnerwith a variety of intro, summary, situations {s} and coin ments {c}presented most often with electronic pages (often wired in onemonolith). External commands {a} on such a course can launch the entirecourse and (if the monolith allows) its specific modes and pages for theuser/learner. Each page can include some of controls (such as buttons,fill in the blanks, menus, . . . ) for the learner. Working with thecourse by acting on its different controls and activating its differentmodes and pages, the learner generates events {e} in this learningenvironment.

Physical models of real object under study. When real objects understudy are not good for some reasons (dangerous, harmful, expensive,complex, distanced, invisible, not open for exploration, too slow/fast,too big/small et cetera), they can be represented with their physicalreplicas, models. Each such model is specially designed to provide thelearner with the same essential situations {S} and controls usuallyprovided by real objects they replace. External commands {a} on them canactivate certain models and certain parts, switch from one model toanother, cause certain modes, functions and steps in the modelfunctioning et cetera. Exploring the model by acting on its controls,the learner generates events {e} in this learning environment.

Real object to learn (e.g., car, engine, dashboard). The domain model160 can include real objects for study. This is a typical for concludingphases of training and for in-job support. Each real object provides thelearner with the real domain situations {d} and real controls forexploration. External commands {a} can bring new domain objects andparts to the learner, change one domain object to another, and (if it isopen enough) cause certain modes, functions and steps in the domainobject behavior, et cetera. Exploring the real object by acting on itscontrols and causing different situations, the learner generates events{e} in this leaning media environment.

Human tutor. The media learning environment 143 can include a humantutor as well. In this case, the logic generator 141 serves as anadvisor for this human tutor on how to teach the learner. Followingthese advices, the human tutor can bring specific domain objects to thelearner, create specific situations, pose the problem, ask question, etcetera. Exploring provided domain, solving tasks, answering questions byacting on controls, the learner generates events {e} in this learningmedia environment.

The Logic-Media Converter

Definition.

The logic-media converter 142 is a part of said tutoring system 140. Itenables communication between the logic generator 141 and the mediaenvironment 143 through different channels (for example:situation/response, comment and control channels). This part of thetutoring system 140 is not innovative as well. It was intentionally kept“as is” in many other learning/tutoring systems to be able to reuse itand to lower a cost of new tutoring system design. The reason for itsconsideration hereinafter is a maximal clarification of an operatingenvironment of the innovative tutoring generator 141.

Functionality.

The media-logic converter realizes two directions of converting:logic-to-media and media-to-logic.

In logic-to-media converting 131, the logic-media converter 142 acceptstutoring decisions {t} from the logic generator 141 and transforms 131them into commands {a} on the learning media environment 143 in order tomaterialize tutoring decisions {t} in a media form, including thespecific situations {s} with controls for learner's actions and comments{c}. By this way it realizes “If (t), then (a)” function.

In opposite media-to-logic converting 133 within the situation/responsechannel, it tracks essential events {e} in the learning mediaenvironment 143 regarding an actually selected assignment (i′), createdsituation (s′) and actual response (k′) of the learner and thengenerates a learning report (i′,s′,k′) to the logic generator 141 foradapting 134. By this way, it realizes “If (e), then (i′,s′,k′)”function. Within the comment and control channels, it tracks learner'scontrol actions, identifies confirmation/control events {e} andtransfers results to the logic generator 141 for decision making 130.

Parameters:

Functioning the logic-media converter 142 depends of learning mediaenvironment 143 and learning activity to support, which can beconsidered as parameters predefined in the phase of providing 100 thetutoring system 140.

The logic-media converter 142 can be customized with adjustableparameters such as: a number of events {l} covered by one report, arequired reliability of learning, behavior identification, et cetera.

Composition

To provide mentioned functionality, the logic-media converter 142includes the following main components, as it is shown in FIG. 14:

-   -   a) A controller 164 for providing the logic-to-media converting        and generating commands {a} on the media environment 143 to        realize each tutoring decision (t) from the logic generator 141;    -   b) A monitor 165 for providing the opposite media-to-logic        converting and reporting learning activity in the media        environment 143 into the logic generator 141.

To support multiple channels in the learning environment 143, the medialogic converter 142 may include multiple components. For example,

-   -   a) a component for control over learning domain situation (d) in        the situation/response channel;    -   b) a component for monitoring the actual domain situation in the        situation/response channel;    -   c) a component for control over presentation of an introduction,        objectives and summary in the comment channel;    -   d) a component for monitoring acceptance of the introduction,        objectives and summary in the comment channel;    -   e) a component for control over comment messages {c} in the        comment channel;    -   f) a component for monitoring comment acceptance confirmation        events in the comment channel;    -   g) a component for control over a progress display;    -   h) a component for choice of manner, the kind and instance of        assignments in the control channel et cetera.

All these components are easily realizable by traditional means. Theinvention does not apply any special restriction on embodiment of thesecomponents.

Operation:

General operation of the situation/response channel of the logic-mediaconverter 142 includes the following steps:

-   -   a) executing 131 tutoring decision (t) by the controller 164, as        it is shown in FIG. 15. It takes control from decision making        step 130 and includes:        -   1. Accepting 166 tutoring decision (t);        -   2. Generating 167 commands {a} onto the media environment            143;        -   Concluding its operation, the controller transfers control            to step 132 with commands to the media environment 143;    -   b) Monitoring 133 by the monitor 165, as it is shown in FIG. 16.        It takes control from step 132 and includes:        -   1. Tracking 170 events {e} in the media environment 143            characterizing an actual situation and learner's response;        -   2. Optional storing 171 the tracked events {e} to be            considered later by authors as a sample situation (s) and a            sample response (k);        -   3. Identifying 172 tracked situation and response by their            comparison against corresponding pre-stored samples (s,k);        -   4. Optional providing 173 the learner with the feedback            message (f);        -   5. Providing 174 the learning report including an identifier            (s′) of identified situation sample and an identifier (k′)            of identified response sample. The part (i′) of the complete            report (i′,s′,k′) characterizing a finally selected instance            of the tutoring assignment can come from the control            channel.

Concluding its operation, the monitor 165 transfers control to step 134with the learning report.

If the monitor 165 is notable to identify the actual behavior (s,k) with100% reliability, it still can produce uncertain beliefs within a range[0-100%] that an actual behavior is similar to some of the samples {s,k}. If the monitor 165 is not able to identify actual behavior (s,k) atall, it can identify it as “unexpected”. It can do it with certaindegree of uncertainty as well. Reporting with uncertainty will beconsidered hereinafter.

General operation of the comment channel of the logic-media converter142 is trivial and includes at least the executing 131 comment decision(c) by the controller 164, which in its turn includes

-   -   a) Accepting 166 tutoring decision (c);    -   b) Generating 167 commands {a} onto the media environment 143.

General operation of the control channel of the logic-media converter142 is trivial as well and includes controlling 131 over supporting 132the learner's choice and monitoring 133 its results by the monitor 165,which comprises:

-   -   a) Tracking 170 control events {e} in the media environment 143        characterizing control actions of the learner, such as choice of        the tutoring manner, the kind of the tutoring assignments and        the instance of tutoring assignments;    -   b) Identifying 172 tracked events, which particularly includes        identifying the tutoring manner, the kind of the tutoring        assignments and an instance (i′) of tutoring assignment selected        by the learner;    -   c) Optional providing 173 the feedback message (f);    -   d) Providing 174 the identifiers of control event into the        generator 141, which particularly can include the identifiers of        tutoring manner, the kind of the tutoring assignments and an        instance (i′) of tutoring assignment selected by the learner.

Embodiments

The specific embodiment of the logic-media converter 142 is dependableof specific embodiment of the media environment 143. Examples caninclude but are not limited to the following instances.

If the media environment 143 is embodied as a paper textbook (just forexplanation), then the controller 164 can be realized as a device (apage-turner) for opening 131 a right page presenting the targetsituation (s) or comment (c) and providing controls (like fill in theblank, a multiple choice menu and a pencil) for the learner. Generatedlearning events {e} (a filled in text, checked up alternatives of themenu) can be traceable, for example, by an optical recognition device.So, the monitor 165 can be realized as a text recognition device forrecognizing a learner entered text on the page, storing samples ofrecognized text, comparing recognized textual response againstpre-stored samples, identifying which pre-stored response is closest tothe pre-stored samples and reporting an identifier (k′) of the closestsample together with an identifier of presented page (s) or (c) to thetutoring logic generator 141.

If the media environment 143 is embodied as an electronic book, then thecontroller 164 can be realized as a program (page-turner) providing aright electronic page to deliver the target situation (s) or comment (c)to the learner. The monitor 165 can be realized as another program fortracking learner's actions on controls (buttons, menus, a multiplechoice) of the e-book storing samples of responses, comparing trackedactions against pre-stored samples, identifying which pre-storedresponse is closest to the pre-stored samples and reporting anidentifier (k′) of the closest sample together with an identifier ofpresented page (s) to the tutoring logic generator 141.

If the media environment 143 is embodied as a loaded audio/video player,then the controller 164 can be realized as a device assigning a righttrack to playback a target audio/video situation (s) or comment (c) forthe learner. The monitor 165 can be realized as another device fortracking learner's actions on controls, storing tracked actions assamples, comparing tracked actions against pre-stored samples,identifying which pre-stored sample is closest to the tracked responseand reporting an identifier (k′) of the closest sample together with anidentifier of presented track (s) to the logic generator 141.

If the media environment 143 is embodied as E-mail device (cell phone,personal digital assistant, computer), then the controller 164 can berealized in any compatible embodiment that allows sending a specificmessage selected by the tutoring logic generator 141 to the learner. Thelearner receives an incoming message in media environment 143 and typeshis/her responsive text {e} In this case, the monitor can be realized ona basis of a natural language processing system, which is able toanalyze the text and provide outcome in a certain form. The monitor 165pre-stores these outcomes as samples and then compares a sample from thelearner against pre-stored samples, identifies which pre-stored sampleis closest to the sample from the learner and reports correspondingidentifier (k′) of the closest sample together with an identifier ofincoming message (s) to the tutoring logic generator 141.

If the media environment 143 is embodied as a set of computer-basedinteractive presentations, then the controller 164 can be realized in acompatible embodiment as a program launching a right interactivepresentation to deliver at least one target situation (s) to thelearner. The learner responds on the presented situation by acting oilembedded controls causing certain events {e} in the learning environment143. The monitor 165 can be realized as another program for trackingresponsive events, storing samples of complete responses, comparing eachnew sample against pre-stored samples, identifying which pre-storedresponse is closest to the new one and reporting an identifier (k′) ofthe closest sample together with an identifier of presented situation(s) to the tutoring logic generator 141.

If the media environment 143 is embodied as a specific computer-basedapplication, then the controller 164 can be realized as a programcausing said application to create at least a target situation (s) forthe learner. Doing that the controller 164 can launch the entireapplication, its specific modes, windows, panels, and steps for thelearner. The monitor 165 can be realized as another program for trackingevents {e} concerning a learning behavior (actual situations andresponsive actions), comparing the tracked behavior with pre-storedones, identifying which pre-stored behavior is the closest to thetracked behavior and reporting identifiers (s′,k′) of the closestbehavior to the logic generator 141.

If The media environment 143 is embodied as a ready made computer-basedtraining course, then it already includes its own media environment,controller 164 and monitor 165. In a favorable case, all that isnecessary to upgrade this course into intelligent tutoring system is toconnect its ready-made components 164-165 with the logic generator 141.In practice, most of known computer-based courses represent a monolithof pre-wired media, logic, controller 164 and monitor 165. But even inthis unfavorable case, sometimes it is possible to overrun an internallogic (prescriptions, scripts, rules) of the course with externaldecisions of the logic generator 141 by connecting them with theexternal controller 164 and/or monitor 165. In this case, the controller164 can be realized as a program overrunning embedded internalprescriptions by assigning the target situation (s) to be presented tothe learner next. Sometimes, the same internal monitor 165 of the coursecan still be used for tracking learner's actions on controls (buttons,menus, a multiple choice), comparing tracked actions with pre-storedones, identifying which pre-stored response is the closest to thetracked response and reporting an identifier (k′) of the closestresponse as well as an identifier of presented situation (s) to thelogic generator 141. It is also possible to use an external program as amonitor 165.

If the media environment 143 is embodied with physical models of realobjects, then the controller 164 can be realized as device acting 131 onsaid physical models to create at least one target situation (s) for thelearner. The monitor 165 can be realized as another device for trackingactual arising events {e} characterizing a learning behavior (actualsituation and learner's actions on controls), comparing tracked behaviorwith pre-stored ones, identifying which expected behavior is the closestto the tracked behavior and reporting identifiers (s′,k′) of closestbehavior to the logic generator 141.

If the media environment 143 is embodied with a real domain object tolearn (like a car, engine, dashboard), then the controller 164 can berealized as a device acting on said domain object to create a desiredsituation for the learner (like engaging a break, starting the engine).The monitor 165 can be realized as another device for tracking arisingevents {e} characterizing a learning behavior (situation and learner'sactions on controls, such as steering wheel, pedals), comparing trackedbehavior with pre-stored ones, identifying which expected behavior isthe closest to the tracked behavior and reporting identifiers (s′,k′) ofclosest behavior to the logic generator 141.

If the media environment 143 includes a human tutor, which uses thelogic generator 141 as an advisor, then the controller 164 can berealized as a messaging device (for example: cell phone, personaldigital assistant, computer) providing the human tutor with instructionson what to do. The monitoring function 133 can be performed manually bythe human tutor with the same messaging device by reporting learner'sbehavior back to the logic generator 141 for adapting 134. In anotherembodiment, the monitor 165 can be an automatic device for trackingarising events {e} characterizing a learning behavior (situation andlearner's actions on controls), comparing tracked behavior withpre-stored ones, identifying which expected behavior is the closest tothe tracked behavior and reporting identifiers (s′,k′) of closestbehavior to the logic generator 141.

Specific Cases of the Generic Tutoring Method

As has been said, each complete assignment (i) defines a targetsituation (s) including domain {d} and problem {p} aspects.

Depending of allocation of control over said aspects of situation (s)among the tutoring generator 141, the learner and the domain 160, thetutoring system 140 can realize different manners and modes ofoperation.

Particularly, the tutoring system 140 can realize:

-   -   a) Single tutoring manners including        -   1. a passive manner of tutoring (case 1), in which the            tutoring generator 141 does not control over domain (d) and            problem (p) aspects of situation (s). This manner can be            realized by            -   A. fixing                -   a. a specific domain 160 defining at least an                    initial domain aspect (d) of the whole learning                    situation (s);                -   b. a specific problem defining at least ail initial                    problem (p) aspect of the situation (s);            -   B. letting                -   a. the domain 160 to evolve the domain aspect (d) of                    situation (s) independently;                -   b. the learner to select the problem (p) aspect of                    situation (s) independently;                -   c. the learner to drive the domain 160 intentionally                    transforming domain (d) and problem (p) aspects of                    situations (s);            -   C. providing the learner with comment message (c) by the                tutoring persona 161 as well as with necessary controls;        -   2. an active manner of tutoring, in which the tutoring            generator 141 participates in forming some or all aspects of            the learning situation (s). This manner can be realize by            -   A. sole controlling over all aspects (d,p) of                situation (s) with logic generator 141 (case 2)                including particularly                -   a. control over domain 160 providing domain                    situations {d} for fixed problem (p). It is an                    example of a supply mode of tutoring.                -   b. control over problem (p) for fixed domain (d).                    This is an example of a testing mode of tutoring.                -   c. control over both domain (d) and problem (p)                    aspect of the situation (s). It is an example of                    mixed supply and testing modes of tutoring.            -   B. sharing control over situation (s) between the                generator 141 and the learner, (case 3):                -   a. letting the generator 141 to assign multiple                    situations [s] for the learner's final choice;                -   b. letting the learner to choose a single                    situation (s) from the pre-selected multiple                    situations [s];                -   c. providing the learner with comment message (c) by                    the tutoring persona 161 as well as with necessary                    controls;                -    Note that the learner and generator 141 may switch                    their turns. The learner can provide a                    pre-selection, then the final selection can be made                    by the generator 141. It is possible but not                    preferred solution.            -   C. sharing control over situation (s) between the                generator 141 and the domain 160 under study, (case 4):                -   a. letting the generator 141 to pre-select multiple                    situations [d,p] for the domain's final selection;                -   b. letting the domain 160 to select/evolve the                    single situation (s), more precisely its domain                    aspect (d);                -   c. providing the learner with comment messages (c)                    by the tutoring persona 161;                -    Note that the domain 160 and generator 141 may                    switch their turns as well. The domain can provide a                    pre-selection (or constraints), then the final                    selection can be made by the generator 141. It is                    possible but not preferred solution because it can                    cause domain-dependency of the generator 141.            -   D. sharing control over situation (s) between the                generator 141, the learner, and the domain 160, (case                5);                -   a. letting the generator 141 to pre-select multiple                    initial situations {d and p aspects} for the                    learner's final choice;                -   b. letting the learner to select a single initial                    situation (d and p aspects) from the pre-selected                    multiple situations;                -   c. letting the domain 160 to evolve the next                    situations (d aspect);                -   d. providing the learner with comment messages (c)                    by the tutoring persona 161 as well as with                    necessary controls;    -   b) Multiple manners by switching between single manners by        -   1. The administrator;        -   2. The learner;        -   3. The tutoring logic generator (case 5).

Let us consider each specific case in more detail.

Case 1. Passive tutoring manner.

The logic generator 141 only observes and comments learning.

This case takes place when

-   -   a) the domain 160 for study and the tutoring persona 161 are        separated in the learning media environment,    -   b) the domain 160 is not tinder control of the generator 141,    -   c) the learner and/or the domain 160 themselves drive the        situation (s) independently of the tutoring generator 141,    -   d) the logic generator 141 controls only the tutoring persona        161 by providing the learner with on-the-fly comments {c}.

The passive tutoring manner is usually realized in job support systems,in non-intrusive training systems as well as in learner-driven learningsystems. In these systems, the worker/learner can select domain (d) towork/learn, problem (p) to perform, explore the domain evolvingdifferent situations {d} and acting on domain's controls providingresponses {k}.

The system 140 can take control at any time after step 104.

Operating 105 the tutoring system 140 in this manner represents aspecific case of the generic tutoring method illustrated in FIG. 7 anddepicted in more detail in FIG. 11. This specific case (case 1) is shownon FIG. 17 and includes the following steps:

-   -   a) Optional accepting 150 the administrative assignment by the        logic generator 141, where        -   1. the parameter of the tutoring manner has “passive” value,        -   2. a fixed tutoring assignment (i′) defines an initial            situation (s) including:            -   A. the specific domain (d) aspect and            -   B. the specific problem (p) aspect;    -   b) Optional preprocessing 151 knowledge/data by the generator        141 by their retrieving from a storage, possible decompressing        and initializing;    -   c) Making 130 tutoring decisions {t} by the generator 141        comprising        -   1. making decision to end tutoring; In this case, it            performs:            -   A. Commenting this decision through the comment channel                including                -   a. executing 131 comment decision with the                    controller 164 by providing commands a(c) on the                    tutoring persona 161;                -   b. Supporting 132 learning activity of the learner                    by providing 176 the learner with comment (c) by the                    tutoring persona 161;                -   c. Monitoring 133 learning activity of the learner                    by optional providing a confirmation of the message                    delivery and acceptance and returning control to the                    decision making 130;            -   B. providing 152 the tutoring report by the logic                generator 141 and            -   C. ending the system operation;        -   2. making achievement decisions, which include diagnostic            decisions, and commenting them through the comment channel            including:            -   A. executing 131 comment decision with the controller                164 by providing commands a(c) on the tutoring persona                161;            -   B. Supporting 132 learning activity by providing 176 the                learner with comment (c) by the tutoring persona;            -   C. Monitoring 133 learning activity of the learner by                optional providing a confirmation of the message                delivery and acceptance and returning control to the                decision making 130;    -   d) Supporting 132 learning activity of the learner through the        situation/response channel including        -   1. Letting 175 the domain model to evolve independently and            provide the learner with the current domain situation (d)            including domain controls to enter his/her response (k)        -   2. letting the learner to select a current problem (p) from            tutoring persona 161, explore the whole situation (s) and            act on available controls;    -   e) monitoring 133 the learning activity of the learner through        the situation/response channel with the monitor 165 and        providing the logic generator 141 with the learning report        including:        -   1. the assignment (i′), which is fixed in this case and            optional due to this reason;        -   2. an identified situation (s′) and        -   3. an identified response (k′) of the learner on the            situation (s′);    -   f) adapting 134 the knowledge/data model of the tutoring logic        generator 141;    -   g) making 130 new tutoring decisions {t} based upon the adapted        knowledge/data model.

After completion of its operation, the system 140 transfers control tothe evaluation step 106.

Case 2. The logic generator controls solely over learning.

In this case, the domain 160 under learner's study and the tutoringpersona 161 in the learning media environment 143 can be (but notnecessarily) separated. The learning environment 143 can be representedeven with the tutoring persona 161 only. Besides providing comments (c),the logic generator 141 is able to control over both the domain 160 andthe tutoring persona 161 by assigning the learning situations {f}, whichincludes the domain (d) and problem (p) aspects, through the controller164 of the logic-media converter 142.

This case is usually realized in educational and interventional trainingapplications for children or learners who are not ready or do not wantto participate in control over their own learning.

The method of active tutoring is a specific case of general tutoringmethod depicted in FIG. 7 and in more detail in FIG. 11. In contrast todescribed passive manner, in the active manner, the learner is notpre-tasked and the tutoring generator 141 has a total control overlearning situations {s}. The learner does not participate in selectinglearning situations {s}.

The system 140 can take control at any time after the step 104.

Operating 105 the tutoring system 140 in this specific case isillustrated in FIG. 18 and includes:

-   -   a) Optional accepting 150 the administrative assignment by the        logic generator 141. The parameter of tutoring manner has the        “active” value. The learner is not specifically pre-tasked in        advance;    -   b) Optional preprocessing 151 knowledge/data for use by        retrieving them from a storage, decompressing and initializing;    -   c) Making 130 tutoring decisions {t} by the logic generator 141        including        -   1. Making decision to end tutoring; If this is a case, then            the next steps are:            -   A. commenting the decision through the comment channel;            -   B. providing 152 the tutoring report by the logic                generator 141 and            -   C. ending the system operation;        -   2. Making achievement decisions (v) and commenting them            through the comment channel;        -   3. Making manner and mode {m} decisions and commenting them            through the comment channel;        -   4. Making assignment (i) of learning situation (s) including            -   A. Assigning domain situation (d) and/or            -   B. Assigning problem (p),            -   C. commenting the assignment through the comment                channel;    -   d) Executing 131 decisions made through the situation/response        channel by providing commands {a} onto the media environment 143        with the controller 164 to execute the tutoring assignment (i)        to provide desired situation (s) including controls for        learner's response;    -   e) supporting 132 learning activity of the learner through the        situation/response channel of the learning media environment 143        including        -   1. providing 175 the learner with the domain aspect (d) of            situation (s) possibly including controls to enter his/her            response (k);        -   2. providing 176 the learner with the problem (p) aspect of            situation (s) possibly including controls to enter his/her            response (k);        -   3. letting 175 the learner explore the domain 160 and act on            available controls;    -   f) monitoring 133 learning activity of the learner through the        situation/response channel of the media environment 143 by the        monitor 165 and providing the logic generator 141 with the        learning report including:        -   1. the assignment (i);        -   2. an identified situation (s′) and        -   3. an identified response (k′) of the learner on this            situation (s′);    -   g) adapting 134 the knowledge/data model of the tutoring logic        generator 141;    -   h) making new tutoring 130 decisions {t} by the logic generator        141 based upon adapted knowledge/data model.

Wherein multiply said commenting the decision through the commentchannel illustrated in FIG. 18 with dashed lines includes:

-   -   a) executing 131 comment decision with the controller 164 by        providing commands a(c) on the tutoring persona 161;    -   b) Supporting 132 learning activity by providing 176 the learner        with comment (c) by the tutoring persona 161;    -   c) monitoring 133 learning activity of the learner by optional        providing a confirmation of the message delivery and acceptance;    -   d) returning control to the decision making 130.

After completion of its operation, the system 140 transfers control tothe evaluation step 106.

Case 3. The logic generator 141 shares active control with the learner.

In this case, the domain 160 under learner's study and the tutoringpersona 161 in the learning media environment 143 can be, but are notnecessarily, separated. The learning environment 143 can be representedeven with the tutoring persona 161 only. Besides all kinds of commentingthrough the comment channel, the logic generator 141 is able to controlover both the domain 160 and the tutoring persona 161 in cooperationwith the learner by providing the learner with multiple assignment [i]through the control channel for his/her own choice of the singleassignment (i) causing the single learning situation (s) in the learningenvironment 143.

This case is usually realized in educational and interventional trainingapplications for adult learners, who want and can handle more controlover their own learning.

The method of active operation is a specific case of general tutoringmethod depicted in FIG. 7 and in more detail in FIG. 11. In contrast topreviously described case 2, the learner is able to control overtutoring assignments [i] and learning situations {s}.

Operation of the system 140 can be started after step 104 and isperformed in accordance with the tutoring phase 105. It includes thefollowing steps as illustrated in FIG. 19:

-   -   a) Optional accepting 150 the administrative assignment by the        logic generator 141. The parameter of tutoring manner has the        “active” value.    -   b) Optional preprocessing 151 knowledge/data for use by        retrieving it from a storage, decompressing and initializing;    -   c) Making 130 tutoring decisions {t} by the logic generator 141        including        -   1. Making decision to end tutoring; In this case, the next            steps are:            -   A. Commenting this decision through the comment channel;            -   B. providing 152 the tutoring report by the logic                generator 141 and            -   C. ending the system operation;        -   2. Making achievement decisions and commenting them through            the comment channel;        -   3. Making manner and mode decisions and commenting them            through the comment channel;        -   4. Making multiple assignment [i] including a set of single            assignments (i) for learner' final choice,        -   5. Making single assignment (i) in cooperation with the            learner (through the control channel) including            -   A. Assigning domain aspect (d), presentation;            -   B. Assigning problem aspect (p), task/question;            -   C. Assigning both domain (d) and problem (p) aspects of                the situation (s);    -   d) executing 131 the tutoring decision (t) including        -   1. in case of multiple assignment [i], providing            commands (a) on the media environment 143 through the            control channel to provide the learner with a choice of a            single assignment (i) from the multiple assignment [i];        -   2. in case of single assignment (i), providing commands (a)            on the media environment 143 through the situation/response            channel of the controller 164 to realize the situation (s)            with corresponding controls for learner responsive actions;    -   e) supporting 132 learning activity of the learner by the        learning media environment 143 including        -   1. in case of multiple assignment [i], supporting learner's            choice of the single assignment (i) from said multiple            assignment [i] through the control channel;        -   2. in case of single assignment (i′), providing 175 the            learner with the domain (d) and/or problem (p) aspect of            situation (s) and controls to enter his/her response (k)            through the situation/response channel;        -   3. letting the learner to explore the situation (s) and act            on available controls;    -   f) monitoring 133 including        -   1. in case of multiple assignment [i], monitoring learner's            choice of single assignment (i) through the control channel,            which transfers control back to the logic generator 141;        -   2. in case of single assignment (i′) defined through the            control channel, monitoring learning activity of the learner            in the media environment 143 through the situation/response            channel of the media-logic converter 142 and providing the            logic generator 141 with the learning report including:            -   A. the single assignment (i′) defined through the                control channel;            -   B. an identified situation (s′) through the                situation/response channel;            -   C. an identified response (k′) of the learner on this                situation (s′) through the situation/response channel;    -   g) adapting 134 the knowledge/data model of the tutoring logic        generator 141;    -   h) making new tutoring 130 decisions by the logic generator 141        based upon adapted knowledge/data.

Wherein multiply said commenting the decision through the commentchannel includes:

-   -   a) executing 131 comment decision with the controller 164 by        providing commands a(c) oil the tutoring persona 161;    -   b) Supporting 132 learning activity by providing 176 the learner        with comment (c) by the tutoring persona;    -   c) Monitoring 133 learning activity of the learner by optional        providing a confirmation of the message delivery and acceptance        and returning control to the decision making 130.

After completion of its operation, the system 140 transfers control tothe evaluation step 106.

Case 4. The logic generator 141 shares control with the domain 160 understudy.

In this case, the domain 160 under learner's study and the tutoringpersona 161 in the learning media environment 143 have to be separated.The logic generator 141 is able to control over both the domain 160 andthe tutoring persona 161 through the situation/response channel byassigning a set of desired domain situations [d] and specific problem(p) to address. The domain 160 then determines the single situation (d)out of pre-selected set [d] of situations. In other words, the tutoringgenerator constrains a domain's freedom for the sake of better learningof the particular learner.

This case can be realized in educational and interventional trainingapplications, which include active learning domains such as simulatorsand games.

The method of active operation is a specific case of general tutoringmethod depicted in FIG. 7 and in more detail in FIG. 11. In contrast todescribed case 3, the learning domain 160 can drive the domain aspect(d) of learning situations {s} itself within the range determined bylogic generator 141.

Operation of the system 140 can be started after step 104 and then it isperformed in accordance with the tutoring phase 105 of the describedmethod. It includes the following steps as depicted in FIG. 20:

-   -   a) Optional accepting 150 the administrative assignment by the        logic generator. The parameter of tutoring manner has the        “active” value.    -   b) Optional preprocessing 151 knowledge/data for use by        retrieving them from a storage, decompressing and initializing;    -   c) Making tutoring 130 tutoring decisions {t} by the logic        generator 141 including:        -   1. Making decision to end tutoring; In this case, the next            steps are:            -   A. Commenting (c) this decision through the comment                channel;            -   B. providing 152 the tutoring report by the logic                generator 141 and            -   C. ending the system operation;        -   2. Making achievement decisions and commenting them through            the comment channel;        -   3. Making manner and mode decisions and commenting them            through the comment channel;        -   4. Making multiple assignment [i] through the control            channel including            -   A. Assigning single problem aspect (p), task/question;            -   B. Assigning a domain situation range [d] to constrain                the domain 175;    -   d) executing 131 the tutoring decisions through the control        channel by providing commands a[d] on the domain 160 with the        controller 164 to constrain the domain 160 on generating 175        situations (d) within the range [d];    -   e) supporting 132 learning activity of the learner through the        situation/response channel with the learning media environment        143 including:        -   1. providing 175 the learner with single domain            situation (d) from said range [d] by the domain 160 as well            as controls to enter his/her response (k);        -   2. providing 176 the learner with problem (p) to solve;        -   3. letting the learner to explore the domain 160 and act on            available controls;    -   f) monitoring 133 learning activity of the learner in the media        environment 143 through the situation/response channel with the        media-logic converter 142 and providing the logic generator 141        with the learning report including:        -   1. single assignment (i′) defined the problem (p) and            situation range [s];        -   2. at least an identified situation (s′) and        -   3. an identified response (k′) of the learner on this            situation (s′);    -   g) adapting 134 the knowledge/data of the tutoring logic        generator 232;    -   h) making tutoring 130 decisions by the logic generator 141.

Wherein multiply said commenting the decision through the commentchannel includes:

-   -   a) executing 131 comment decision with the controller 164 by        providing commands a(c) on the tutoring persona 161;    -   b) Supporting 132 learning activity by providing 176 the learner        with comment (c) by the tutoring persona;    -   c) Monitoring 133 learning activity of the learner by optional        providing a confirmation of the message acceptance and returning        control to the decision making 130.

After completion of its operation, the system 140 transfers control tothe evaluation step 106.

Case 5. The logic generator 141 shares control with the learner and thedomain 160 under study.

This case combines case 3 and 4 together in two phases. On the firstphase, the generator 141 narrows the choice for the domain 160. On thesecond phase, the domain 160 narrows the choice for the learner. Thelearner makes the final choice of the next tutoring assignment (i) torealize corresponding learning situation (s).

The Tutoring Logic Generator

Definition:

The tutoring logic generator 141 is an innovative part of the entiretutoring system 140 that makes it “intelligent”. It represents a “brain”of the tutoring system 140.

Functionality:

In communication with the administrator, said tutoring generator 141receives an administrative assignment and returns the tutoring reportabout learner's progress.

Said administrative assignment defines the learner, the instructionalunit, and tutoring manner to begin with. It also includes parameters forcustomizing a tutoring style realized by the tutoring generator. Thereare other parameters of the tutoring generator, such as adaptationcoefficients (INC and DEC), which can be used by instructors for finetuning desired speed of its adaptation process. All parameters will bedescribed hereinafter.

In communication with the learning media environment 143 through themedia-logic converter 142, the logic generator 141 receives learningactivity reports, adapts its knowledge/data and makes tutoringdecisions.

The tutoring decisions {t} can include but are not limited to

-   -   a) A plurality of achievement decisions {v};    -   b) A couple of manner decisions (passive or active);    -   c) A triplet of mode decisions (supply, testing, or diagnosing);    -   d) Tutoring assignment decisions {i} of the following three        categories:        -   1. A single assignment (i) of at least one learning            situation (s) by the generator 141, which does not leave any            choice for the learner;        -   2. Multiple assignment [i] by the generator 141 representing            a set of single assignments (i) for the following learner's            own choice of one single assignment;        -   3. Rated assignment (Weight [i]) by the generator 141            representing said multiple assignment [i], where each single            assignments (i) is associated with a personal utility            (Weight) value for informed learner's choice of one single            assignment.

The learning activity report represents:

-   -   a) An identifier (i′) of realized single assignment;    -   b) Identifier (s′) of identified situation and    -   c) Identifier (k′) of identified response.        Composition.

As it is illustrated in FIG. 21, the tutoring logic generator 141includes the following main coupled modules:

-   -   a) the knowledge/data model 180, which represents a nesting        hierarchy of the following modules:        -   1. a memory 182 including:            -   A. a reusable framework 183 including:                -   a. specific tutoring data 184;    -   b) the reusable tutoring engine 181 that obtains the learning        reports {i′,s′,k′} and generates tutoring decisions {t} based        upon said tutoring knowledge model 180. It includes:        -   1. an optional pre-processor 185 for data 184 pre-selecting,            preparing and initializing;        -   2. a decision maker 186 for making 130 tutoring decisions            {t} based upon knowledge/data model 180;        -   3. a processor 187 for specific data 184 adapting 134            including:            -   A. Updater 188 for data 184 updating based upon learning                reports,            -   B. Reviser 189 for data 184 revising based upon                decisions made;            -   C. Optional reporter 190 for progress reporting to the                administrator;            -   D. Optional improver 191 for specific data 184 improving                Operation.

Operating the tutoring generator 141 is a part of the tutoring system140 operating 105 depicted in general in FIG. 7 and in more detail inFIG. 11. Separately this part is illustrated in FIG. 22.

It can take control at any time after step 104.

On setup stage, operating the tutoring generator 141 can include:

-   -   a) Optional accepting 150 the administrative assignment from the        administrator and storing it in the memory 182 framework 183 as        a part of the specific data 184;    -   b) Optional pre-processing 151 stored specific data 184, which        can include        -   1. selecting and retrieving necessary data;        -   2. transforming data from storage format to implementation            format and        -   3. initiating data for their use in the tutoring session.

In tutoring session, that is initiated by a user (a learner orinstructor), the tutoring engine 181 makes 130 tutoring decisions {t} bydecision maker 186 including a decision to stop or continue tutoringbased upon available data 184.

If it decided to stop the tutoring, then the reporter 190 prepares 152 atutoring report.

If it decided to continue tutoring, then decision maker 186 makes 130other decisions {t} and transfers control to the controller 164 forexecuting 131. Then it gets back control from the monitor 165 of themedia-logic converter 142 on step 133, obtains available data throughthe control channel and the learning report (i′,s′,k′) through thesituation/response channel.

Through the control channel, illustrated with the dashed arrow, thedecision maker 186 obtains data from its partner in decision makingprocess, the learner, including chosen tutoring manner, a type and maybe the instance (i′) of tutoring assignment.

When the tutoring generator 141 obtains the learning report (i′,s′,k′)through the situation/response channel, its processor 187 adapts 134specific data 184 and enables new tutoring decisions based upon adaptedspecific data 184.

Adapting 134 data 184 includes:

-   -   a) specific data 184 updating by updater 188 based upon learning        report (i′,s′,k′);    -   b) specific data 184 revising by reviser 189 based upon        diagnostic decisions made;    -   c) optional progress report preparing by reporter 190    -   d) optional knowledge/data 184 improving by improver 191.

On final stage, the reporter 190 submits the tutoring report to theadministrator, ends its operation and transfer control to the evaluatingstep 106.

This generic operating of the generator 141 has its specificity in eachspecific case 1-5.

Case 1. The passive (non-intrusive) tutoring manner can be determined bythe administrative assignment on step 150 or at any other time by thelearner through the control channel. The problem (p) aspect of thesituation (s) is assigned on this step too. The decision maker 186 doesnot provide any assignments. It lets the domain 160 and/or the learnerdrive learning situations {s}The updater 188 “observes” the leaningactivity through learning reports (i′,s′,k′), updates 134 its data 184and then the decision maker 186 makes 130 occasional achievementdecisions {v} and possibly the manner decision to switch from thecurrent passive to the active tutoring manner.

Case 2. In active (interventional) manner, the decision maker 186 makes130 tutoring decisions {t}, which include achievement {v}, manner, modeand assignment {i} decisions. For each tutoring assignment (i′), theupdater 188 obtains the learning report (i′,s′,k′) from the monitor 165,updates 134 its data 184 and enables new tutoring decisions. If decisionmaker 186 made a diagnostic decision, then the reviser 189 revises thedata 184 to enable automatic re-instructing of the learner from thediagnosed cause of faults detected.

Cases 3-5. In active (interventional) manner, the decision maker 186shares decision making 130 with the learner and the domain 160.Particularly, in case of providing multiple [i] or rated assignment(Weight[i]), the learner chooses a single assignment (i′) him/herselfthrough the control channel. The updater 188 gets back the learningreport (i′,s′,k′) from the monitor 165, updates 134 its data 184 andenables new tutoring decisions. Again if decision maker 186 made adiagnostic decision, then the reviser 189 revises the data 184 to enableautomatic re-instructing of the learner from the diagnosed cause offaults detected.

Knowledge/Data Model and its Framework

The tutoring knowledge/data model 180 is a part of said generator 141,which includes domain/learner-specific data 184 in memory 182 organizedinto the uniform reusable framework 183. See FIG. 23.

The memory 182 used for knowledge/data model 180 can be a standardrandom access type in order to support standard operations such as: datarecording, storing, updating and retrieving. The memory 182 can besubdivided into long term memory and operative memory to support realtime data processing in the tutoring engine 181. Data stored in longterm memory can be pre-processed 151 for more effective use in theoperative memory.

The uniform reusable tutoring knowledge/data framework 183 represents aspecial organization of the memory 182 and includes:

-   -   a) an administrator-generator communication protocol 195;    -   b) a learning space framework 203 representing        learner-independent instructional knowledge referenced to        specific instructional unit;    -   c) a learner data framework 204 referenced to the learner for        personal adaptation of the tutoring generator 141;

Note: The tutoring knowledge/data framework 183, due to symmetry withthe administrator-generator communication protocol 195, has to have agenerator-converter communication protocol (including tutoringassignment and learning report framework) in order to supportcommunication between the generator 141 and converter 142. That is fairand said generator-converter protocol will be provided for thesituation/response channel by said learning space 203 and learner data204 frameworks and described hereinafter.

The specific data 184 are filled in the uniform framework 183.

Administrator-Generator Communication Protocol

As illustrated iii FIG. 23, the administrator-generator communicationprotocol 195 is a part of the tutoring knowledge/data framework 183. Itincludes:

-   -   a) Administrative assignment framework 201 and    -   b) Tutoring report framework 202.        Administrative Assignment and its Framework

The administrative assignment is a part of knowledge/data model 180. Asa whole it includes a memory (a carrier), generic framework(placeholders or variables) and specific data (values). In preferableembodiment, the administrative assignment uses a part of common memory182 organized in the administrative assignment framework 201, whichrepresents a part of said reusable framework 183.

The administrative assignment framework 201 is also a part of theuniform communication protocol 195 between the administrator and thetutoring generator 141. It includes the following memory placeholders tobe filled with specific data 184 in order to customize the tutoringgenerator 141:

-   -   a) a learner identifier (l),    -   b) a domain or instructional unit identifier (u);    -   c) a plurality of domain-independent and learner-independent        tutoring parameters including at a minimum:        -   1. Tutoring manner to begin with (passive, active or to be            determined by the learner),        -   2. Supply threshold, ST,        -   3. Testing Threshold, TT,        -   4. Diagnosing Threshold, DT.

Where,

-   -   a) said supply threshold (ST) defines required reliability of        content supply, specifying what is sufficient in learning        content supply to overcome known unreliability of learners with        redundant set of learning activities;    -   b) said testing threshold (TT) defines required reliability of        testing, specifying what is sufficient to overcome known        unreliability of testing (in particular, possibility of guessing        in multiple choice questions) with redundant set of problems and        questions;    -   c) said diagnosing threshold (DT) defines required reliability        of diagnosing, specifying what is sufficient to isolate a single        cause of learners' faults from others.

These three parameters have the same range of possible values 0-1. Theirdefault values can be the same: ST=T-r=DT=0.9.

Tutoring Report and its Framework

The tutoring report is a part of knowledge/data model 180. As a whole itincludes a memory (carrier), generic framework (placeholder-s orvariables) and specific data (values). In preferable embodiment, thetutoring report can use a part of common memory 182 organized in thetutoring report framework 202, which represents a part of said reusableframework 183.

A tutoring report framework 202 is also a part the uniform communicationprotocol 195 between the administrative system and the tutoringgenerator 141. It represents a learning progress of the learner in oneof possible forms (for example, a traditional score, mastery profile, ora learner state model hereinafter). On demand, it can include more data.The invention does not imply any specific format for said report, butrecommends using the learner data described hereinafter as the mostinformative representation of a learning progress.

Learning Space Model and its Framework

A real learning process of a particular learner is very complex andhidden phenomena, which cannot be directly observed and exactlymeasured. However, human tutors used to manage this very complex processpretty good with their mental representations and uncertain knowledge.

So does the tutoring generator 141. But in contrast with human tutor'simplicit informal representations, the tutoring generator 141 uses anexplicit formal representation of tutoring knowledge 180 that isnecessary and sufficient for automatic generation of a tutoring 105 bythe tutoring engine 181.

The learning space model is a part of knowledge/data model 180, whichrepresents instructional declarative knowledge of the tutoring generator141 about the learning process of any learner from a target audience atany time point within a specific instructional unit and domain. Ingeneral, it includes a memory (carrier), generic framework (placeholdersor variables) and specific data (values). In preferable embodiment, thelearning space model uses a part of common memory 182 organized in thelearning space framework 203, which represents a part of said reusableframework 183.

As illustrated in FIG. 24, the learning space framework 203 includes thefollowing parts:

-   -   a) a state space framework 205 for representing important but        not traceable aspects of a learning process in said learning        environment 143;    -   b) a behavior space framework 206 representing important        traceable aspects of learning process in said learning        environment 143 referenced to expected learning behaviors and        particularly defining space holders for possible learning        reports;    -   c) a state-behavior relation framework 207 integrating said        state space framework 205 with said behavior space framework 206        into the whole learning space framework 203.

Note that any traditional instructional unit is designed for a targetaudience of learners and is not a priori adapted to any particularlearner. In our case, such an instructional unit can be represented withthe entire tutoring system 140 with empty learner data framework 204 andtherefore include:

-   -   a) Specific media environment 143;    -   b) Specific media-logic converter 142;    -   c) Uniform tutoring engine 181 and    -   d) Uniform framework-based knowledge/data model 180, which in        its turn includes:        -   1. Specific learning space model 203.

In contrast to such a holistic definition of the instructional unit,there is another definition of the instructional unit as a coursewarefor playback. In accordance with it, a specific instructional unlit isdefined as a specific (declarative) courseware separately from itsuniform (procedural) player. In accordance with this definition, theintelligent instructional unit can be defined separately from itsuniform multimedia (procedural) players and tutoring logic (procedural)engine 181 as well and represent the (declarative) part of tutoringsystem 140 including

-   -   a) in its media part:        -   1. Specific learning resources of the media environment 143            and        -   2. Specific media-logic relations of the converter 142 and,    -   b) in its logic part,        -   1. specific learning space model 203 filled in uniform            framework 180.

To represent general logical properties of the entire intelligentinstructional unit, the specific data of the learning space model 203can be easily aggregated into the following integral data:

-   -   a) Instructional unit identifier (u);    -   b) Manners coverage {passive, active};    -   c) Mode coverage (supply, testing, diagnosing);    -   d) Difficulty level range {very easy, easy, medium, difficult,        very difficult}    -   e) Testing threshold limit {up to 1};    -   f) Supply threshold limit {up to 1};    -   g) Diagnosing threshold limit {up to 1};    -   h) Properties range, such as:        -   1. Languages {English, Spanish, French, . . . };        -   2. Age of target audience {6-10, 10-13, . . . }.

As can be seen now, the administrative assignment determines specificlogical properties of entire instructional unit within their possibleranges.

State Space Model and its Framework

A state space model is a part of the learning space model, whichrepresents important but directly untraceable aspects of learningprocess of each particular learner at any time within specificinstructional unit.

As a whole it includes a memory (carrier), generic framework(placeholders or variables) and specific data (values). In preferableembodiment, the state space model shares common memory 182 organized inthe state space framework 205, which represents a part of said learningspace framework 203.

The state space framework 205 includes:

-   -   a) a plurality of learning objectives {j} of the instructional        unit, where each learning objective is something to be taught        and learned such as: specific expertise, knowledge, skills,        attitude, aptitude, beliefs, preferences, opinions, etc.    -   b) a plurality of achievement states of each learning objective        including at least:        -   1. no-achievement state, NAS;        -   2. supplied achievement state, SAS, and        -   3. demonstrated achievement state, DAS.            -   Where the supplied achievement state is realized due to                supplying the learner with learning                activities/resources/situations for learning,                demonstrated achievement state is due to successful                testing of the learner, and no-achievement state because                of insufficient supply or a learning fault.            -   Note that in contrast to a definition of known Bayesian                models of learning states and so named “knowledge                spaces” (Dietrich Albert Cord Hockemeyer, 1997), which                represent said OR space, specified here states are not                mutually exclusive. They can partially coexist and thus                represent said AND-OR space. Specifically,                no-achievement state can coexist with the supplied                achievement state, the latter can coexist with the                demonstrated achievement state, but the latter cannot                coexist with no-achievement state.    -   c) a prerequisite relation among objective achievement states.        Each objective achievement state is not static and can be        changed due to some (internal or external) reasons.        Specifically, any no-achievement state can transit to the        supplied achievement state due to supplying the learner with        learning situation/resources. The supplied achievement state in        its turn is able to transit to the demonstrated achievement        state in case of testing success. In contrast, a fault result of        testing can provide a transition of the supplied achievement        state into the no-achievement state again to initiate resupply.        A state transition diagram is summarized in FIG. 25. In short,        the no-achievement state is a prerequisite to the supplied        achievement state, which is a prerequisite to the demonstrated        achievement state:    -   d) A prerequisite relation among achievement states of different        learning objectives. Very often an achievement of one learning        objective requires achievement of some other prerequisite or        enabling objectives. It means that supplied or demonstrated        achievement of one objective can contribute to supply of another        objective. These dependencies are usually defined by course        authors. In general case, authors have no exact knowledge about        prerequisite relations. But understanding the domain and        conceiving a certain tutoring strategy, they can provide some,        at least not very certain (fuzzy), beliefs about existence of        prerequisite relation among each pair of objectives. The        tutoring generator can use such prerequisite beliefs including        local prerequisite beliefs LPRB(j,h) that said supplied        achievement state of one objective (h) requires prior at least        the supplied achievement state of another objective (j). See        FIG. 26 for a table representation of the prerequisite        relations. Note that by standard transposition operation, said        local prerequisite beliefs LPRB(j,h) can be easily transformed        into local succeed beliefs LSCB(j,h)=LPRB(h,j).

Said plurality of learning objectives {j} of the instructional unit (u)includes baseline objectives, which have no prerequisite objectivesdefined with the LPRB(j,h), and terminal objectives, which have nosucceed objectives defined with the LSCB(j,h).

In simple visual form, the state space model can be sketched as anetwork of objectives connected with prerequisite binary relations. Seeexample in FIG. 27.

In more detailed tree form, the state space model is illustrated in FIG.28.

Behavior Space Model and its Framework

The behavior space model is a part of said learning space modelrepresenting important traceable aspects of learning process. Itsframework 206 includes

-   -   a) An identifier (i) of at least one tutoring assignment or a        plurality of them {i},    -   b) An identifier (s) of at least one learning situation or a        plurality of them {s} and    -   c) An identifier (k) of at least one possible response or        plurality of them {k}.

Despite of a possible variety of control sharing options between thegenerator 141, the learner and the domain 160 (see cases 1-5 above), thefinal cooperative decision is just a single tutoring assignment (i) torealize in media environment 143. In general, each tutoring assignment(i) can generate more than one learning situations {s} in learningenvironment 143. Despite of a variety and complexity of possiblelearner's responses on each learning situation (s), the final result ofits identification represents just a single identifier (k) of thelearner response.

As has been said, the completely defined situation (s) includes what isgiven (d) and what is required to do (p) in the domain. That is why eachspecific learning situation (s) is able to initiate a learning activityof the learner. As a rule, the learning media environment 143 includescontrols for learner's responsive actions and the monitor 165 includessensors to track actual situations and actions. Of course, the learnercan perform uncountable number of unexpected actions as well, but all ofthem can be categorized just as a single “unexpected” response anddenoted with one identifier (K+1).

Assuming all of these, the behavior space model includes the followingdata in general:

-   -   a) a plurality of identifiers {i} of corresponding plurality of        single tutoring assignments in active tutoring manner (cases        2-5). In passive tutoring manner (case 1), it includes only one        fixed assignment (i), which actually can be changed by the        learner or domain 160:    -   b) a plurality of situation identifiers {s} of a corresponding        plurality of learning situations provided by the leaning media        environment 143;    -   c) a plurality of response identifiers {k=1,2, . . . ,K,K+1} of        a corresponding plurality of expected responses {k=1,2, . . .        ,K} of the learner in each learning situation (s) from said        plurality of learning situations {s} extended with the extra        identifier (K+1), which denotes all possible unexpected        responses of the learner in the situation(s).

A sample of the behavior space framework 206 for each assignment (i) ina table form is given in FIG. 29. Each column in the table (i) denotessituation (s). Each row (k) denotes expected responses of the learner.“1” in intersection of the column (s) and row (k) means a possiblebehavior (i→s→k). If there is no certain evidence that the situation (s)provokes the response (k), then “1” can be replaced with correspondingbehavior belief BB(s,k). It is a possible fuzzy extension of introduceddeterministic behavior space framework 206.

The described behavior space framework 206 defines in general saidcommunication protocol of the tutoring generator 141 with themedia-logic converter 142.

Note that traditional fixed scripts/flowcharts used in widely spreadregular computer-based education and training systems can be describedpotentially within the same framework 206 just because the inventedlogic generator 141 and traditional scripts/flowchart are supposed tosimulate the same ideal external tutoring behavior. The problem is thatthe traditional manual scripting in advance of what the tutoringgenerator 141 automatically generates in real time operating with anyparticular learner is practically impossible.

Tutoring Assignment and its Framework

A tutoring assignment is a tutoring decision to realize specificlearning situation (s) in the learning environment 143 for the learner.Particularly realization of said specific learning situation (s) in thelearning environment 143 can be done by providing a uniform media playerwith a corresponding learning media resource.

In general, the learner and domain 160 can participate in the situationdetermination (see cases 3-5). To support such a cooperative assignmentof learning situation (s), tutoring generator begins with pre-selectingthe multiple assignment [i], which includes a set of single assignments.Then the learner and/or the domain model 160 can narrow this set down toone single assignment (i) to realize.

All available single tutoring assignments {i} are pre-stored in thegenerator memory 182. Corresponding memory is organized in a uniformtutoring assignment framework 211, as it is shown in FIG. 30, andincludes placeholders for the following data:

-   -   a) an identifier (i) of single tutoring assignment;    -   b) an optional identifier (s) of at least one target learning        situation to be created;    -   c) an optional identifier of learning resource (r) of the media        environment 143, which is necessary to generate said learning        situation (s). This direct reference to the learning        resource (r) can help to simplify possibly a complex chain of        logic-media conversion of each tutoring assignment (i) into        specific command a(s) only the learning environment 143 to        realize the target situation (s);    -   d) optional identifiers of tutoring modes (supply, testing or        diagnosing) prescribed for the assignment by the author. By        default the tutoring generator 141 can select all assignments        automatically within each tutoring mode, but an author is        welcome to prescribe in advance the best modes for each        assignment;    -   e) a difficulty level of the tutoring assignment (i) comparable        with said difficulty limit, DL.    -   f) a plurality of assignment properties corresponding to        personal requirements of the learner and preferences of the        learner from the learner data framework 204.    -   g) an implementation status (IS) having a set of values        including at least “implemented” (IS=1) and “not implemented”        (IS=0) values;    -   h) an optional reference to corresponding state-behavior        relation described hereinafter.        Learning Situation and its Logical Framework

In the learning environment 143, each specific media representation ofthe domain 160 and problem (p) for the learner can be quite different(see possible embodiments of the learning environment 143 above) andinclude different controls.

Possible examples are:

-   -   a) a static presentation slide with the “Next” button,    -   b) a “multiple choice” question with selectable alternatives:    -   c) a “fill in the blank” question with means to type in the        text;    -   d) an “essay” kind of question with means to enter the text;    -   e) a dynamically evolving simulation with specific controls        (buttons, joystick, etc);    -   f) a static moment in the game with specific controls available        at the moment;    -   g) a dynamic voice/speech playback with controls: stop, play,        pause, et cetera.

In accordance with its role in learning state framework 203, eachlearner situation (s) should be aimed to provide at least one of thefollowing:

-   -   a) To supply the achievement state of at least one learning        objective by the learner;    -   b) To check the demonstrated achievement state of at least one        learning objective;    -   c) To diagnose the no-achievement state of at least one learning        objective.

Despite of this variety, a mathematical representation (or logic behindthe media) is quite simple:

-   -   it is just an identifier (s) of the situation existing in media        environment 143.        Learner Response and its Logical Framework

In the learning environment 143, physical controls for learner's actioncan be quite different (see possible embodiments of the learningenvironment 143 above).

In the monitor 165 of the media-logic converter 142, sensors forcapturing learner's action events {e} on these controls can be quitedifferent as well (see possible embodiments of the media-logic converter142 above).

Possible examples are:

-   -   a) a click of “Next” button in a presentation slide,    -   b) a specific alternative selected by the student in a “multiple        choice” question,    -   c) a text typed by the student in the “fill in the blank” type        of question,    -   d) a text entered by the student in the essay type of question,    -   e) a sequence of hits on buttons of the simulator;    -   f) a voice/speech of the student,    -   g) a multi-dimensional trajectory of the joystick in a game et        cetera.

In accordance with its role in the learning space framework 203, eachresponse (k) should be able to provide at least one of the following:

-   -   a) Evidence of the achievement state of at least one learning        objective by the learner;    -   b) Evidence of the demonstrated achievement state of at least        one learning objective;    -   c) Evidence of the no-achievement state of at least one learning        objective.

Tracking and identifying learner's responses in the monitor 165 can bevery complex. It is a separate problem that has known solutions, whichare supposed to be implemented in the monitor 165. But the logicalrepresentation of identification results in the tutoring generator 141from the monitor 165 is very simple and represents just a set ofidentifiers {k} of expected responses. Its minimal value is k=1, if onlyone alternative of correct response has been predefined. It can be equalas well to k=1, 2, 3, . . . up to its maximal value k=K denoting anumber of all expected sample responses of the learner available in themonitor 165 for identification of actual response of the learner in thesituation (s).

In extension to said set of expected response identifiers {k}, acomplete set of all possible response identifiers includes also anidentifier (k=K+1) denoting a plurality of all unexpected responses,which is impossible or not necessary to predefine.

Optionally, in order to support traditional scoring, each possibleidentifier (k) can be complemented with a specific numerical valueexpressing algebraic contribution of corresponding response to theentire score.

Learning Report and its Framework

A learning report is an instance or case of said behavior space modelrepresenting a message from the monitor 165 to the tutoring generator141.

Its framework 212 includes the following placeholders for specific data:

-   -   a) an identifier (i′) of single tutoring assignment chosen        collectively by the generator, domain and learner, which in        general can be unknown a priory by the generator 141;    -   b) an identifier (s′) of an identified learning situation from        said plurality of expected learning situations {s}, which is the        closest (in similarity) to the actual situation experienced by        the learner. In general, the identified situation (s′) can        differ from the target situation (s), which the tutoring        generator intended to create, due to a generally unpredictable        behavior of the domain and the learner;    -   c) an identifier (k′) of all identified response from said        plurality of expected {k′=1, 2, 3, . . . ,K} and unexpected        responses (k′=K+1), which is the closest (in similarity) to        actual response of the learner in situation (s′).

In case the monitor 165 is not able to identify actual situation (s)and/or response (k) completely up to 100% reliability, it still canproduce and the generator is able to accept uncertain beliefs that anactual situation (s′) and response (k′) are similar to available samples{s} and {k}. In this case, the learning report is more complex andincludes the following:

-   -   a) the identifier (i);    -   b) a set of Situation Beliefs SB{s},    -   c) a set of response Beliefs RB{k}.

Note: Introduced here ontology/vocabulary of intelligent tutoring can beconsidered as well as a core of traditional script/flowchart-basedSharable Content Objects (SCO) from Sharable Content Object ReferenceModel. Indeed, widely used static linear and branching sequences ofSharable Content Assets (SCA) within Sharable Content Object (SCO) canbe described with introduced here terms including:

-   -   a) an identifier (i) of specific learning activity associated        with specific learning resource (r);    -   b) an identifier of learner's response (k) in said learning        activity (i) associated with said learning resource (r);    -   c) association of each learner's response (k) with the next        learning activity (i) to be assigned to the learner.

Note: Traditional scripts/flowcharts represent just a (manual, static,superficial media-based) shortcut of the (automatic, dynamic, soundlogic-based) tutoring generator 141. Despite their quite differentinternal structure, their external behavior is supposed to be the same:both assign the next learning activity (i′) depending of learner'sresponse (k′).

State-Behavior Relation and its Framework

A state behavior relation is a part of said learning space model thatintegrates the state space model and the behavior space model together.This relation provides an opportunity of internal interpretation ofexternal learning behavior and by this way supports making main tutoringdecisions.

For example, the correct response (k) of the learner in the problemsituation (s) demonstrates the achievement state of some objectives (j).In other words, each correct behavior sample (i→s→k) provides anevidence of the demonstrated achievement state of certain objectiveswith certain beliefs, namely local demonstrating beliefs, LDB(j).

In contrast, a fault response (k) of the learner in the same problemsituation (s) provides an evidence of the no-achievement state of someobjectives, namely local fault beliefs, LFB(j).

A response (k) of the learner confirming just an acceptance of alearning domain situation(s) for study can evidence the suppliedachievement state, namely local supplying beliefs, LSB(j), of certainobjectives.

In general case, a learner response (k) on a situation (s) can bepartially successful and partially faulty at the same time and thusprovides LDB(j) and LFB(j), each on its own subsets of learningobjectives. It can also evidence an acceptance of certain learningmaterial and provide LSB(j) on certain learning objectives.

In general, the state-behavior relation includes a plurality of beliefsthat a typical learner from a target audience has specific achievementstates of each learning objective (i) from the state space model, ifsaid learner realizes a specific behavior instance (i,s,k) from saidbehavior space model.

Accordingly, as illustrated in FIG. 31, the uniform state-behaviorrelation framework 207 comprises placeholders for the followingplurality of beliefs:

-   -   a) a local demonstrating belief LDB(i,s,k,j) that the learning        behavior instance (i,s,k) evidences the demonstrated achievement        state of a learning objective (j) from said plurality of        learning objectives {j};    -   b) a local supplying belief LSB(i,s,k,j) that said learning        behavior instance (i,s,k) evidences said supplied achievement        state of a learning objective (j) from said plurality of        learning objectives {j};    -   c) a local fault belief LFB(i,s,k,j) that said learning behavior        instance (i,s,k) evidences said no-achievement state of said        learning objective (j) from said plurality of learning        objectives {j}.

Note, that in a special case, when only one correct response ispredefined, which means that k=K=1, there is no need to storeLFB(i,s,k,j) in the memory 182 because said LFB(i,s,k,j)=LDB(i,s,k,j).

Learner Data Model and its Framework

The learner data model is a part of tutoring knowledge/data model, whichrepresents generator's knowledge/data of the particular learner in thetutoring loop. The learner data framework 204 is a set ofdomain-independent and learner-independent placeholders in the memory182 for personal data of the learner, which is important for tutoringdynamic adaptation. It includes:

-   -   a) a learner state model defined on the basis of said state        space framework 205;    -   b) a learner behavior model defined on the basis of said        behavior space framework 206;    -   c) a personal data model defined on the basis of said personal        data framework 213;        Personal Data Model and its Framework

Personal data model is a part of said learner data model. Its uniformframework 213 includes a plurality of possible requirements of thelearner, plurality of his/her possible preferences, and plurality ofcurrent tutoring style parameters.

The possible requirements of the learner are supposed to be strict,non-negotiable and cannot be compromised by the tutoring generator 141(but can be edited by the learner), while preferences are soft,negotiable and can be compromised by the tutoring generator as well asedited by the learner.

In preferred embodiment, requirements and preferences frameworks arepresented in a checklist form. See the self explanatory example ofrequirement checklist in FIG. 32 and self explanatory example ofpreference checklist in FIG. 33.

Prior to a learning session, the tutoring style parameters can beassigned for the learner by the instructor, by the tutoring engine bydefault, or selected by the learner him/herself. Then during thesession, they will be automatically adjusted by the processor 187. Inpreferred embodiment, the framework 213 includes the followingadjustable parameters:

-   -   a) a difficulty limit, DL,    -   b) a testing delay limit, TDL,    -   c) a fault tolerance limit, FTL, and    -   d) a desired type of tutoring assignments (TAT) in active        tutoring manner (multiple, rating or single).

An initial value of the difficulty limit, DL, can be selected from thefollowing common list: {very easy, easy, medium, difficult, verydifficult}. Each qualitative value of DL has a correspondingquantitative value: 1-5. Default value DL=medium=2 is recommended.

Initial value of the Testing Delay limit, TDL, denoting a number oflearning objectives to supply prior their achievement testing, is fromone (1) objective up to a total number of all learning objectives (J).Default value TDL=3 is recommended.

Initial value of the fault tolerance limit FTL, denoting a maximaltolerable sum of no-achievement: beliefs sufficient to switch thetesting mode into the diagnosing mode, can be selected from 0.001 up toa total number of learning objectives (J). Default value FTL=0.3 isrecommended.

Desired type of tutoring assignments TAT specifies one of the followingtypes of tutoring assignments:

-   -   a) a multiple tutoring assignment, which assigns a subset [i] of        single tutoring assignments from said plurality of available        single tutoring assignments {i} to enable guided personal        learner's choice of one single assignment (i); TAT=multiple;    -   b) a rating tutoring assignment (weight[i]), which rates said        pre-selected subset [i] of single tutoring assignments to enable        informed personal learner's own choice of zone single assignment        (i); TAT=rating;    -   c) a single tutoring assignment from said plurality of available        single tutoring assignments {i}. This option is considered as a        default type of tutoring assignments, TAT=single.        Learner State Model and its Framework

A learner state model is a part of said learner data model thatpositions the learner in said state space model. Its uniform framework214 includes placeholders for the following specific data:

-   -   a) a set of beliefs of the tutoring generator 141 that the        learner has specific achievement states of each specific        learning objective (j). All these beliefs together represent        knowledge of the tutoring generator about current state of the        learner in the learning state space. At a minimum, for each        learning objective (j) they include        -   1. a no-achievement belief NAB(j) corresponding to said            no-achievement state,        -   2. a supplied achievement belief SAB(j) corresponding to            said supplied achievement state and        -   3. a demonstrated achievement belief DAB(j) corresponding to            said demonstrated achievement state.        -   All these beliefs have the same range of possible values            [0-1].        -   Their initial values are NAB(j)=SAB(j)=DAB(j)=0.        -   The current values of these beliefs are changed during            operation of the generator 141 and should be resumed if the            learner quits the instructional unit to be able to restart            the next session from the same state.    -   b) a learning prospect P(j) defining a direction of a learning        progress through the plurality of learning objectives. It is        necessary to keep the same direction of tutoring to terminal        objectives to prevent occasional jumping of a tutoring        discourse.    -   c) Optionally, a set of necessary learning objectives from said        plurality of learning objectives {j}. It represents a subset [j]        of learning objective set {j} specially selected by the learner        to achieve within the instructional unit (u) and all their        enabling objectives defined with local prerequisite beliefs        LPRB(j,h). Isolation and further use of only these objectives        [j] allows focusing of tutoring activity on exactly what the        learner wants to achieve within the instructional unit.    -   d) Optionally, a plurality of approved achievement states from        said plurality of achievement states of each learning objective        (j), which are necessary to make strategic (high-stake) tutoring        decisions, such as: learning of the entire unit is successfully        completed, content supply of the entire unit is successfully        completed, and fault diagnosing is successfully completed. These        data are calculated from already available NAB(j), SAB(j) and        DAB(j) and include:    -   1. an approved demonstrated achievement state ADAS, which        corresponding demonstrated achievement belief DAB(j) is equal or        exceeds said testing threshold, DT,    -   2. an approved supplied achievement state ASAS, which        corresponding supplied achievement belief SAB(j) is equal or        exceeds said supply threshold, ST, and    -   3. an approved no-achievement state ANAS, which no-achievement        belief NAB(j) exceeds no-achievement beliefs NAB(h) of all other        learning objectives {where h is not equal to j} by said        diagnosing threshold, DT.

The core learner state model can be represented in table form. See FIG.34.

In simple visual form, the learner state model can be represented as acolored objective network. See FIG. 35, where each objective is paintedwith a different color pattern according to its state. In preferableembodiment, green color pattern means the supplied achievement state,blue color pattern means the demonstrated achievement state, and redcolor pattern means no-achievement state. Belief values can bedisplayed, for example, with different intensity, radius or filling ofsaid color patterns in each objective.

Learner Behavior Model and its Framework

The learning behavior model is a part of learner data model. It isdefined as a specific instance or case of the behavior space model andincludes:

-   -   a) the identifier of assignment (i),    -   b) the identifier of situation (s),    -   c) the identifier of response (k).

In case if the monitor 165 of the media-logic converter 142 is not ableto identify actual situation (s) and response (k) completely, thegenerator 141 can accept and process uncertain beliefs of the monitor165 that an actual situation and response are similar to availablesamples {s} and {k}. In this more generic case, the learner behaviormodel includes:

-   -   a) the identifier of assignment (i);    -   b) the set of situation Beliefs SB{s},    -   c) the set of response Beliefs RB{k}.

As can be seen the learner behavior model is just the learning report ofthe monitor 165 about learning activity of the learner into thegenerator 141.

Generator-Converter Communication Protocol

The generator-converter communication protocol is a part of the tutoringknowledge/data framework 183. Its framework includes already described:

-   -   a) tutoring assignment framework 211 and    -   b) learning report framework 212.        Data from Authors

In process of an instructional unit design, authors are supported withauthoring tools, which include described uniform frameworks, to fill intheir domain/tasks-specific logical (vs media) knowledge and data 184comprising:

-   -   a) A set of learning objectives {j} of the instructional unit,    -   b) A tutoring strategy described with local prerequisite beliefs        LPRB(j,h) that the supplied achievement state of one        objective (h) requires prior at least the supplied achievement        state of another objective (j). It can be presented in table        form (see FIG. 26) or preferable network form (see FIG. 27).    -   c) A tutoring style defined with the following parameters:        -   1. Tutoring manner (passive, active, or both),        -   2. said testing threshold, TT;        -   3. said supply threshold, ST;        -   4. said diagnosing threshold, DT;    -   d) Identifiers of learning situations {s} to recognize in        passive tutoring manner and/or to create in active tutoring        manner;    -   e) Every single tutoring assignment (i) specifications, as it is        illustrated in FIG. 30.    -   f) Identifiers of expected responses {k} on each learning        situation (s) for each tutoring assignment (i);    -   g) The state-behavior relation defined with the following        beliefs:        -   1. local demonstrating belief LDB(i,s,k,j),        -   2. local supplying belief LSB(i,s,k,j),        -   3. local fault belief LFB(i,s,k,j).

Optionally. Authors can even advice the tutoring generator 141 what todo by direct prescribing the next tutoring assignment (i) to certainbehavior instances [i,s,k]. These prescriptions will allow running theintelligent instructional unit by non-intelligent regular sequencingengines, such as the current engines in the SCORM run-time environment.It allows increasing the reusability of the intelligent courseware.

Sometimes, the logical authoring by manual description of all these datacan be labor consuming as well. To simplify it, it is possible, at leastpartially, to perform a manual demonstration and interpretation oflearning behavior in the media environment 143 by the author. In thiscase, the author selects each tutoring assignment (i) in available mediaenvironment 143, demonstrates a sample of expected learner's activity(i,s,k) and map it into the objective {j}state network. To support thiskind of advanced authoring, the authoring tool should be able toassociate demonstrated samples (i,s,k) and {j} into correspondingbeliefs LDB(i,s,k,j), LSB(i,s,k,j), and LFB(i,s,k,j). It is just datastoring and technically obvious.

Data from Instructors

Instructors can manage the learning process within the universe providedby authors of instructional units and specify the following data in theadministrative assignment:

-   -   a) learner identifier (1),    -   b) instructional unit identifier (u),    -   c) tutoring style parameters (within a range predefined by        authors):        -   1. Tutoring manner (passive, active, or both),        -   2. Supply Threshold, IT        -   3. Testing Threshold, TT,        -   4. Diagnosing Threshold, DT, as well as        -   5. Difficulty limit, DL,        -   6. Testing delay limit, TDL,        -   7. Fault tolerance limit, FTL,        -   8. Types of tutoring assignments (multiple, rating, single,            or all)            Data from Learners

Learners can control over their own learning process within optionspredefined for them by instructors. The learner is welcome to select aninstructional unit (u), tutoring manner to begin with, and tutoringstyle parameters within the range pre-defined by instructors including:

-   -   a) Difficulty limit, DL    -   b) Testing delay limit, TDL,    -   c) Fault tolerance limit, FTL,    -   d) Types of tutoring assignments (TAT=multiple, rating, single,        or all).        Data Pre-Processing

Original data 184 from authors can be stored in the generator memory 182and be processed during run-time operation of the generator 141. Ifthere is a need to accelerate a run-time operation, original data 184from authors can be preprocessed 151 prior their run-lime use in atutoring session.

In preferred embodiment, data 184 obtained originally for authors arepre-processed by the tutoring generator 141 prior their usage. Thepreprocessing 151 includes:

-   -   a) transformation,    -   b) extrapolating,    -   c) integrating,    -   d) pre-selecting and    -   e) preparing.

(a) Transformation of prerequisite relations into succeed relations isnecessary for instructional planning of learning supply. Thistransformation is performed by a standard transposition of said localprerequisite beliefs LPRB(j,h) into succeed beliefs LSCB(j,h) byswapping index (j) with index (h) in said local prerequisite beliefsLPRB(j,h). So, succeed beliefs LSB(j,h)=LPRB(h,j).

(b) Extrapolating local beliefs into global ones.

This is necessary for instructional planning in order to provide thetutoring generator 141 with capability to look forward (to envisageinfluence of each assignment/situation) up to terminal learningobjectives and to look backward (to estimate response background orbacktrack causes of faults) down to baseline learning objectives withinthe instructional unit. Mathematically, extrapolating can be performedon the basis of standard multiplication of a matrix LPRB(j,h) orLSCB(j,h) with a vector of local beliefs: LSB(j), LDB(j) or LFB(j). Itcan be done as well in a classic Bayesian manner. But in the simplestand preferred embodiment, it is recommended to usc a standard MaxMinoperation.

Specifically,

-   -   global prerequisite beliefs GPRB(j,h) are defined procedurally        for all terminal objectives with step by step backtracking all        prerequisite objectives defined within corresponding local        prerequisite beliefs LPRB(j,h) down to the baseline objectives,        GPRB(j,h)←LPRB(j,h);    -   global succeed beliefs GSCB(j,h) can be defined procedurally for        all baseline objectives with step by step toward tracking its        succeed objectives defined with corresponding local succeed        beliefs LSCB(j,h) up to the terminal objectives,        GSCB(j,h)←LSCB(j,h).

As has been said, local succeed beliefs LSCB(j,h) are just atransposition of the local prerequisite beliefs LPRB(j,h),LSCB(j,h)=LPRB(h,j). Analogically, the global succeed beliefs GSCB(j,h)are a transposition of the global prerequisite beliefs GPRB(j,h),GSCB(j,h)=GPRB(h,j). Thus, described above procedure of definingGSCB(j,h) can be performed by simple transposition of GPRB(h,j).

Specifically,

-   -   global supplying beliefs GSB(i,s,k,j) represent a result of        extrapolating said local supplying beliefs LSB(i,s,k,j) with        said global succeed beliefs GSCB(j,h) up to terminal learning        objectives, which have no succeed learning objectives, defined        by local succeed beliefs LSCB(j,h):        ${{GSB}( {i,s,k,j} )} = {\underset{h}{Max}{Min}{\{ {{{LSB}( {i,s,k,h} )}*{{GSCB}( {j,h} )}} \}.}}$

Global demonstrating beliefs GDB(i,s,k,j) represent a result ofextrapolating said local demonstrating beliefs LDB(i,s,k,j) with saidglobal prerequisite beliefs GPRB(j,h) down to baseline learningobjectives, which have no prerequisite learning objectives, defined bylocal prerequisite beliefs LPRB(j,h):${{GDB}( {i,s,k,j} )} = {\underset{h}{Max}{Min}{\{ {{{LDB}( {i,s,k,h} )}*{{GPRB}( {j,h} )}} \}.}}$

Global fault beliefs GFB(i,s,k,j) represent a result of extrapolatingsaid local fault beliefs LFB(i,s,k,j) with said global prerequisitebeliefs GPRB(j,h) down to baseline learning objectives, which have noprerequisite learning objectives, defined by local prerequisite beliefsLPRB(j,h):${{GFB}( {i,s,k,j} )} = {\underset{h}{Max}{Min}{\{ {{{LFB}( {i,s,k,h} )}*{{GPRB}( {j,h} )}} \}.}}$

(c) Integrating beliefs.

Integrating is necessary for instructional planning in order to providethe tutoring generator 141 with a “big picture” and exclude noisydetails. Mathematically, it can be performed by a standard integratingoperation across a value range of a variable to exclude. Particularly,the fuzzy algebra including Max, Min and other standard operations canbe used for these purposes. But in preferred embodiment, we use standardMean operation, which implementation is much wider.

Specifically, integrated local demonstrating beliefs ILDB(i,s,j)represent said local demonstrating beliefs LDB(i,s,k,j) aggregatedacross all expected responses {k=1,2, . . . K} of the behavior spacemodel. In the simplest and preferred embodiment, they are calculatedwith the standard Mean operation according to the following formula:${{ILDB}( {i,s,j} )} = {\sum\limits_{k = 1}^{K}{{{LDB}( {i,s,k,j} )}/{K.}}}$

Integrated local supplying (beliefs ILSB(i,s,j) represent said localsupplying beliefs LSB(i,s,k,j) aggregated across all expected responses{k=1,2, . . . K} of the behavior state model. In simplest and predefinedembodiment, they are calculated analogically:${{ILSB}( {i,s,j} )} = {\sum\limits_{k = 1}^{K}{{{LSB}( {i,s,k,j} )}/{K.}}}$

Integrated global demonstrating beliefs IGDB(i,s,j) represent anextrapolation of said integrated local demonstrating belief'sILDB(i,s,j) with said global prerequisite beliefs GPRB(j,h) down tobaseline learning objectives, which have no prerequisite learningobjectives, defined with said local prerequisite beliefs LPRB(j,h). Insimplest and preferred embodiment, they are calculated with thefollowing formula:${{IGDB}( {i,s,j} )} = {\underset{h}{Max}{{{Min}\lbrack {{{ILDB}( {i,s,j} )},{{GPRB}( {j,h} )}} \rbrack}.}}$

Demonstrating background beliefs DBB(i,s,j) represent a pureextrapolation of said integrated global demonstrating beliefsIGDB(i,s,j) over said integrated local demonstrating belief ILDB(i,s,j)down to baseline learning objectives. In simplest and preferredembodiment, they are calculated with the following formulaDBB(i,s,j)=IGDB(i,s,j)−ILDB(i,s,j).

Supplying background beliefs SBB(i,s,j) represent a pure extrapolationof said integrated local supplying beliefs ILDB(i,s,j) with said globalprerequisite beliefs GPRB(j,h) down to baseline learning objectives. Insimplest and preferred embodiment, they are calculated with thefollowing formula:${{SBB}( {i,s,j} )} = {{\underset{h}{Max}{{Min}\lbrack {{{ILSB}( {i,s,j} )},{{GPRB}( {j,h} )}} \rbrack}} - {{{ILSB}( {i,s,j} )}.}}$

Integrated global supplying beliefs IGSB(i,s,j) represent anextrapolation of said integrated local supplying beliefs ILSB(i,s,j)with said global succeed beliefs GSCB(j,h) up to terminal learningobjectives, which have no succeed learning objectives defined with saidLSCB(j,h). In simplest and preferred embodiment, they are calculated inaccordance with the following formula${{IGSB}( {i,s,j} )} = {\underset{h}{Max}{{{Min}\lbrack {{{ILSB}( {i,s,j} )},{{GSCB}( {j,h} )}} \rbrack}.}}$

Integrated global fault beliefs IGFB(i,s,j) can be defined as anextrapolation of said integrated local fault beliefs ILFB(i,s,j) withsaid global prerequisite beliefs GPRB(j,h) down to baseline learningobjectives, which have no prerequisite learning objectives defined withsaid LPRB(j,h). But in simplest and preferred embodiment, they can beapproximated with said integrated global demonstrating beliefsIGDB(i,s,j),IGFB(i,s,j)=IGDB(i,s,j).

(d) Pre-selecting.

Pre-selecting personally appropriate assignments for the learner allowsreducing a number of options in a real-time selection of the nextassignment in active tutoring manner. This operation checks how eachcandidate assignment properties meets personal requirements of eachlearner. Not matching assignments are rejected from a list ofassignments for the learner.

(e) Preparing.

The most effective adaptive diagnosing of fault causes takes asignificant amount of operations. Fortunately, it allows preparing somedata in advance as follows:

Pre-selecting tutoring assignments from said plurality of tutoringassignments {i}, which prescribed mode (see FIG. 30) is diagnosing ortesting.

Pre-selecting tutoring assignments from remaining plurality of tutoringassignments, which corresponding GDB(i,s,k,j)>0 on at least one learningobjective (j) of diagnosing interest.

In each pre-selected assignment (i), pre-selecting only diagnosticallymeaningful responses (k), where [GDB(i,s,k,j) or GFB(i,s,k,j)]>0 on atleast one learning objective (j) and exclusion of all other responses.See a table of diagnostic data in FIG. 38.

Stretching remaining GDB(i,s,k,j) and GFB(i,s,k,j) in one sequence byreplacing the same index (k) in both of them with one single index (q)with different values for GDB(i,s,q,j) and GFB(i,s,q,j). See a table ofdiagnostic data in FIG. 39.

Inversing and renaming GDB(i,s,q,j) by the following operation:MN(i,s,q,j)←1−GDB(i,s,q,j);

Renaming GFB(i,s,q,j) by the following operation:MN(i,s,q,j)<GFB(i,s,q,j);

-   -   For each single tutoring assignment (i) corresponding to        situation (s), calculating sum MS(i,s,j) of MN(i,s,q,j) across        all possible responses q=1,2, . . . ,2K+1;        ${{{MS}( {i,s,j} )} = {\sum\limits_{q = 1}^{{2K} + 1}{{MN}( {i,s,q,j} )}}};$    -   Normalizing MN(i,s,q,j) for each assignment (i) corresponding to        situation (s):        MN(i,s,q,j)←MN(i,s,q,j)/MS(i,s,j);

Resulting data MN(i,s,q,j) are ready for run-time adaptive diagnosing.See FIG. 39.

In the simplest embodiment, each single assignment (i) creates a singlelearning situation (s). It means that (i) can be arranged to be equal(s) and overall dimension of tutoring data 184 can be decreased.

Knowledge/Data Verification

Specific knowledge/data 184 for the knowledge/data model 180 should bemutually consistent as well as necessary and sufficient for solving alltutoring tasks by said tutoring engine 181 in desired tutoring manners.

For passive tutoring manner:

To enable reliable testing of all learning objectives {j}, a predefinedplurality of identifiable learning situations {s} within a soleassignment (i′) should be sufficient to cover all declared learningobjectives {j} with predefined reliability defined with the testingthreshold, TT.

Particularly, the sufficiency of the situation set {I} for passivetesting can be checked by combining their integrated local demonstratingbeliefs ILDB(i′,s,j) in accordance with the following procedure:

-   -   a) Initialization DAB(j)=0;    -   b) For all (s) beginning from s=1 and incrementing with step 1        up to s=S and for all (j) beginning from j=1 and incrementing        with step 1 up to j=J Calculating:        DAB(j)←DAB(j)+ILDB(i′,s,j)−DAB(j)*ILDB(i′,s,j);    -   c) Checking tip if for all j=1, 2,3, . . . , J corresponding        DAB(j)>=TT, then the set {s} of learning situations is        sufficient for testing all plurality of learning objectives,        otherwise    -   d) Defining more situations and repeating the step (b) of        calculating until sufficiency on the step (c).

To enable testing/diagnosing focused down to each single leaningobjective, each learning objective (j) should be covered with at leastone distinct behavior (i′,s,k) in the sole assignment (i′)characterizing achievement of only this specific learning objective(well, may be together with some prerequisite objectives) withpredefined reliability, TT.

To enable on-the-fly diagnostic remediation focused down to each singlelearning objective, each learning objective (j) should be provided inadvance with at least one extra supply assignment with lowest difficultylevel (which is actually a remediation) able to correct theno-achievement state of diagnosed learning objective with at leastpredefined reliability, ST.

For active tutoring manner:

To enable bulk supply and testing of all learning objectives, a wholeplurality of available assignments {i} of learning situations {S} shouldcover all declared learning objectives {j} with predefined reliability.

Particularly, this sufficiency can be checked by combining integratedlocal supply and demonstrating beliefs in accordance the followingprocedure:

-   -   a) Initialization DAB(j)=SAB(j)=0;    -   b) For all (i) beginning from i=1 and incrementing with step 1        Lip to i=1        -   for all (s) beginning from s=1 and incrementing with step 1            tip to s=S and            -   for all j beginning from j=1 and incrementing with step                1 tip to j=J        -   Calculating            -   1. DAB(j)←DAB(j)+ILDB(i,s,j)−DAB(j)*ILDB(i,s,j);            -   2. SAB(j)←SAB(j)+ILSB(i,s,j)−SAB(j)*ILSB(i,s,j)];    -   c) Checking up if all SAB(j)>=ST, then the set {i} of tutoring        assignments is sufficient to supply the set {j} of learning        objectives;    -   d) Checking up if all DAB(j)>=TT, then the set {i} of tutoring        assignments is sufficient to test the set {j} of learning        objectives;    -   e) Otherwise extent the set of tutoring assignments {i} and        return to calculating (b) until sufficiency.

To enable (optional) the most effective supply, testing and diagnosingall learning objectives, available plurality of tutoring assignments {i}and teaming situations {f} should be diversified enough to meetdiversity of possible learning states.

To enable just in point (remedy) supply, testing and diagnosing focuseddown to each single objective, each learning objective (j) should beprovided with at least one single supply assignment with the lowestdifficulty level and a single testing/diagnosing assignment eachcovering only this specific learning objective (j) with at leastpredefined reliability defined with corresponding ST and FT.

To enable (optional) highly personalized selection of tutoringassignments for each particular learner, the plurality of all tutoringassignments {i} and learning situations {s} should be diversified enoughto cover all diversity of personal requirements and preferences of alllearners from the target audience.

At a minimum, to provide necessary controllability and observability oflearning process within an instructional unit, each learning objective(j) from the plurality of all learning objectives {j} of aninstructional unit should form a self-sufficient quartet including:

-   -   a) Single learning objective (j) itself;    -   b) Reference to prerequisite learning objectives [h];    -   c) A single supply assignment (i) of minimal difficulty, which        is sufficient to supply or remedy achievement of said single        objective (j) in case of all its prerequisite objectives [j] are        already supplied sufficiently;    -   d) A single testing/diagnosing assignment (i), which is        sufficient to test achievement of said single objective (j) may        be together with all or some of its prerequisite objectives.        Data Initializing

If the learner begins the unit or instruction from scratch, then thetutoring generator 141 has no any beliefs about his/her personallearning state. Initially they are equal to zero:

-   -   a) no-achievement belief NAB(j)=0;    -   b) supplied achievement belief SAB(j)=0;    -   c) demonstrated achievement belief DAB(j)=0.

An initial value of the difficulty limit, DL, can be selected by thelearner personally from the following SCORM-compliant list: {very easy,easy, medium, difficult, very difficult}. Each qualitative value of DL,has a corresponding quantitative value: 1-5 Default value DL=medium=2 isrecommended.

Initial value of the Testing Delay Limit, TDL can be selected by thelearner personally or by instructor from one objective (TDL=1) up to atotal number of learning objectives (TDL=J). Default value TDL=3 isrecommended.

Initial value of the fault tolerance limit FTL can be selected by aninstructor/learner from FTL=0.001 tip to a total number of learningobjectives (FTL=J). Default value FTL=0.3 is recommended.

If a learner quits a unit, his/her current personal data are stored inthe long term memory. When he/she returns, stored data are resumed inthe operative memory 182 and used as initial ones.

The Tutoring Engine

Environment.

The tutoring engine 181 is a domain/learner-independent part of thetutoring logic generator 141 of intelligent tutoring 105.

Parameters:

It coupled with the knowledge/data model 180 that particularly providesit with the administrative assignment including identifiers of thelearner (l), instructional unit (u) and tutoring parameters, which inturn includes as a minimum: the tutoring manner (passive or active),supply threshold (ST), testing threshold (TT), and diagnosing threshold(DT). The list of parameters can be extended with parameters foradvanced fine tuning the generator including coefficients (INC and DEC)defining a desired speed of adaptation process.

Functions.

During the session it obtains the learning reports {i′,s′,k′} from themedia-logic converter 142, processes the knowledge/data model 180 andmakes all kind of tutoring decisions {t}.

In passive manner the engine 181 makes main achievement {v} and mannerdecisions as well as assigns corresponding comments {c} through thecomment channel.

In active manner, it additionally selects its internal tutoring mode andan external tutoring assignment (i) to realize a specific learningsituation (s) for the learner in learning environment 143 through thesituation/response channel. Through available control channel it canalso accept the type of assignments chosen by the learner in thelearning environment 143.

Concluding the tutoring session, it generates a tutoring reportoptionally.

Composition.

The generator engine 181 includes the optional pre-processor 185 andobligatory decision maker 186 and processor 187 coupled together asdepicted in FIG. 40. The processor 187, in its turn, includes theupdater 188 and reviser 189. Optionally it can include also the reporter190 and improver 191. All components 188-190 of the processor 187 areconnected to the decision maker 186.

Operation.

The flowchart of the engine operation is illustrated in FIG. 41.

It can take control at any time after step 104.

In the beginning of each tutoring session, the preprocessor 185 canprepare all necessary data for operating the decision maker 186.

In the passive manner, the decision maker 186 uses the knowledge/datamodel 180 to make 130 main tutoring decisions including decisions to endtutoring, put a diagnosis, and switch to the active manner. Then itassigns corresponding comment (c) for the learner through the commentchannel of the media-logic converter 142 and the media environment 143.

In the active tutoring manner, decision maker 186 additionally decideswhich tutoring mode (supply, testing, diagnosing) to execute and whichfirst (then next) tutoring assignment (i) to select and realize throughthe situation/response channel (optionally adjusted by the learner byselecting the desired type of tutoring assignments through the controlchannel).

In both passive manner when assignment (i′) is fixed and in activemanner when assignment (i′) is made, after making any decision (t), thedecision maker 186 transfer control to controller 164 for its executing131.

Then the updater 188 gets control back from step 133 and accepts thebehavior report (i′,s′,k′) from the monitor 165 of the media-logicconverter 142.

If decision maker 186 made a diagnostic decision, then reviser 189performs revising 216 of knowledge/data 184 and returns control to thedecision maker 186 for making 130 new tutoring decisions.

Optional improver 191 monitors success and faults of learning/tutoringtogether with corresponding beliefs used for decisions made 130. Then itincrement those beliefs that supported successful decisions anddecrement beliefs that caused fault tutoring decisions. More detail isprovided hereinafter.

Such operating continues until the decision maker 186 (or the learner)decides to end tutoring. Concluding the tutoring session, the reporter190 can provide 152 the tutoring report, end its operation and transfercontrol to evaluating step 106.

The Decision Maker

Environment.

The decision maker 186 is a part of the generator engine 181 providingmain tutoring decisions {t} in real time of the learning process.

Parameters:

It is indirectly customized by the administrative assignment availablein knowledge/data model 180 including identifiers of the learner (l),instructional unit (u) and tutoring parameters which in its turnincludes at a minimum: the tutoring manner to begin with (passive oractive), supply threshold (IT), testing threshold (TT), and diagnosingthreshold (DT).

Functions.

The decision maker 186 processes the knowledge/data model 180 andprovides the media-logic converter 142 with the following decisions {t}to realize in the media environment: 143:

-   -   a) decisions to begin or end tutoring process with assigning        corresponding introduction or summary of the session through the        comment channel;    -   b) achievement decisions {v} with assigning corresponding        comments through the comment channel;    -   c) manner decisions (passive or active) with assigning        corresponding comments through the comment channel;    -   d) inode decisions (supply, testing or diagnosing) with        assigning corresponding comments through the comment channel;    -   e) assignment decisions {i} to provide the learner with specific        learning situations {s} through the situation/response channel.

In the active manner of tutoring, it can accept desired type of tutoringassignments chosen by the learner through the control channel.

Composition.

The decision maker 186 has an external input from the knowledge/datamodel 180 and internally comprises interconnected strategic 220, tactic221 and operative 222 decision makers. See FIG. 42. An output of thestrategic decision maker 220 is connected with an input of the tacticdecision maker 221. Another output of the strategic decision maker 220and an output of the tactic decision maker 221 are connected with aninput of operative decision maker 222. The operative decision maker 222has an external output to the controller 164 of the media-logicconverter 142 and another external input for the learner's controlactions mediated with the control channel. Decision makers 220 and 221have two-directional external connections with media-logic converter142. Strategic decision maker 220 has also external connections with thereviser 189 and reporter 190 not shown in FIG. 42.

Operation.

The decision maker 186 can start its operation at any time when theknowledge/data model 180 is ready. Particularly, it can take controlfrom preprocessing step 151 or adapting step 134. The flowchart of itsoperating is depicted in FIG. 43.

First the strategic decision maker 220 analyses current knowledge/data180 trying to identify typical cases among the approved achievementstates and, in case of success, makes 223 corresponding achievementdecisions. Decisions made can be commented for the learner by thetutoring persona 161 through the comment channel, which returns controlto the strategic decision maker 220 again to continue its operation 223.Learner can participate in strategic decision making through the controlchannel by ending the session.

Particularly, the strategic decision maker 220 decides when to endtutoring. If it is the case, then it can optionally command the reporter190 to provide 152 the administrator with the tutoring report. In caseof diagnostic decisions, the strategic decision maker 220 transferscontrol to the reviser 189 and gets it back when revising is completed.It is not shown in FIG. 43.

If the strategic decision maker 220 did not make any decisions, thencontrol is transferred to the tactic decision maker 221, otherwisecontrol is transferred to the operative decision maker 222.

The tactic decision maker 221 also analyzes the knowledge/data 180trying to define 224 if there is a need to switch the current tutoringmode to another one. Decisions made by the tactic decision maker 221 canbe commented for the learner in media environment 143 by the tutoringpersona 161 through the comment channel returning control to the tacticdecision maker 221 again. In any case, was the decision made or not, anoutput of the tactic decision maker 221 is the current tutoring mode andcontrol is transferred to the operative decision maker 222.

In active tutoring manner, the operative decision maker 222 analyses theknowledge/data 180 taking into account the current mode and selects thenext tutoring assignment (i′) to realize 131 by the controller 164 inthe media environment 143 for the learner through the situation/responsechannel. It also can share this decision making process with the learnerby pre-selecting possible assignments for learner's final choice,mediated through the control channel of media environment 143.

In passive tutoring manner, the operative decision maker 222 skips itsoperation letting the domain 160 or the learner define the next learningsituation.

The Strategic Decision Maker

Environment.

The strategic decision maker 220 is a part of the decision maker 186.

Parameters:

It is customized by the same administrative assignment available inknowledge/data model 180 including identifiers of the learner (l),instructional unit (u) and tutoring parameters, which in turn includesat a minimum: supply threshold (IT), testing threshold (TT), anddiagnosing threshold (DT).

Function.

The strategic decision maker 220 analyses current knowledge/data model180 trying to identify approved achievement states of the learningobjectives and typical cases among them. In case of success it makescorresponding achievement {v} decisions. The learner can participate indecision making process as well through the control channel ofcommunication.

Data to analyze include:

-   -   a) Supplied achievement beliefs SAB(j),    -   b) Demonstrated achievement beliefs DAB(j),    -   c) No-achievement beliefs NAB(j),    -   d) The supply threshold, ST,    -   e) The testing threshold, TT,    -   i) The diagnosing threshold, DT.

Achievement states to identify:

-   -   a) approved demonstrated achievement state, ADAS;    -   b) approved supplied achievement state, ASAS, and    -   c) approved no-achievement state, ANAS.

Typical cases to identify:

-   -   a) All objectives are in the approved demonstrated achievement        state.    -   b) At least one terminal objective transits into the approved        demonstrated achievement state.    -   c) All objectives are in the approved supplied achievement        state.    -   d) At least one terminal objective transits into the approved        supplied achievement state.    -   e) At least one learning objective (j) transits into the        approved non-achievement state, a diagnosis case.    -   f) All objectives are in the initial state (all beliefs are        zero, it is a baseline state).

Tutoring decisions to make:

-   -   a) End tutoring;    -   b) Assign the reporter 190 to generate the tutoring report;    -   c) Praise a learner for progress;    -   d) Provide the learner with the summary;    -   e) Start testing mode and comment this decision;    -   f) Put diagnosis, inform the learner about diagnosed learning        objective;    -   g) Revise the learner state model (based on framework 214);    -   h) Provide the learner with the introduction;    -   i) Start supply mode and comment this decision.        Composition.

The strategic decision maker includes at least three identifying rules230-232, six decision rules 233-238, an assigner of the tutoring report,a switch to testing mode and a switch to supply mode.

Identifying rules 230-232 are not ordered and include the following:

-   -   a) Rule 230: If the demonstrated achievement belief DAB(j) is        equal or exceeds said testing threshold (TT), then the        objective (j) is in the approved demonstrated achievement state;    -   b) Rule 231: If the supplied achievement belief SAB(j) is equal        or exceeds said supply threshold (ST), then the objective (j) is        in the approved supplied achievement state;    -   c) Rule 232: If the no-achievement belief NAB(j) exceeds        no-achievement beliefs NAB(h) of all other learning objectives        {where h is not equal to j} by said diagnosing threshold (DT),        then the objective (j) is in the approved no-achievement state.

Decision rules 233-238, which are arranged in a linear sequence,include:

-   -   a) Rule 233: If the approved demonstrated achievement state is        identified for all (terminal) objectives {j}, then praise the        learner providing the summary, assign reporter 190 to generate        the tutoring report and end tutoring.    -   b) Rule 234: If the approved demonstrated achievement state is        identified for the first time for at least one terminal        objective (j), then praise the learner. This is an optional        rule.    -   c) Rule 235: If the approved supplied achievement state is        identified for all learning objectives {j}, then praise the        learner and, in case of active manner, start the testing mode.    -   d) Rule 236: If the approved supplied achievement state is        identified for the first the for at least one terminal objective        (j), then praise a learner and, in case of active manner, start        the testing mode. This is an optional rule.    -   e) Rule 237: If the approved non-achievement state is identified        (a diagnosis is posed), then inform a learner about cause of        his/her error(s) made, in case of passive manner, advise the        learner to switch to active mode to remedy it, and in case of        active manner, start revising 216.    -   f) Rule 238: If an initial state (all beliefs are zero) is        identified for all objectives {j}, then provide the learner with        an introduction to the unit of instruction and, in case of        active manner, start the supply mode of tutoring.        Operation.

The strategic decision maker 220 takes control from the preprocessingstep 151 by the pre-processor 185 or from the adapting step 134 by theprocessor 187.

It analyses said data, identifies said approved achievement states,detects said typical cases, makes said decisions, assigns the reporter190 to provide the tutoring report, and switches to testing 240 andsupply 241 modes in active tutoring manner. The flowchart in FIG. 44 isself explanatory.

Concluding its operation, the strategic decision maker 220 transferscontrol to tactic decision making 224 by the tactic decision maker 221,if there was not: any strategic decision made. Otherwise it transferscontrol to operative decision making 225 by the operative decision maker222.

The table representation of strategic decision making with examples ofpossible commenting is given in FIG. 45.

The Tactic Decision Maker

Environment.

The tactic decision maker 221 is a part of the decision maker 186.

Parameters

It is indirectly customized by identifiers of the learner (l),instructional unit (u) and the tutoring parameters: the current tutoringmanner (passive or active), supply threshold (IT), and testing threshold(TT).

Additionally, the tactic decision maker 221 takes into account thetolerance level TL and testing delay TD from the personal data framework213.

Function.

In passive tutoring manner, the tactic decision maker 221 canautomatically switch to a passive diagnosing mode to find causes ofdetected faults as well as offer the learner to switch to the activemanner of tutoring for these faults remediation.

In active tutoring manner, it selects the current tutoring mode from acomplete set of tutoring mode including supply, testing and diagnosingmodes.

Data to analyze:

-   -   a) Supplied achievement beliefs SAB(j),    -   b) Demonstrated achievement beliefs DAB(j),    -   c) No-achievement beliefs NAB(j),    -   d) Supply threshold, ST,    -   e) Testing threshold, TT.

Typical cases to identify:

-   -   a) faults are not tolerable anymore;    -   b) local supply is sufficient;    -   c) local testing is sufficient.

Tutoring decisions to make:

-   -   a) Start diagnosing mode;    -   b) Start testing mode;    -   c) Start supply mode.        Composition.

The tactic decision maker 221 includes three decisive rules 242-244arranged in a linear order, optional switch 245 to the active manner, aninitiator 246 of diagnosing data, and three mode switches 247-249.

Rule 242: If for all objectives {j} Sum of NAB(j)>=FTL, then offer thelearner to switch to active manner and independently of his/her choiceinitiate diagnosing data and start diagnosing mode (in passive or activemanner).

Rule 243: If number of objectives in the approved supplied state [whereSAB(j)>=ST] exceeds a number of objectives in the approved demonstratedstate [where DAB(j)>=TT] by testing delay parameter, TD, or more, thenstart testing mode.

Rule 244: If all objectives {j} in the approved supplied state (whereSAB(j)>=ST) are also in the demonstrated achievement state (whereDAB(j)>=TT), then start supply mode.

Operation.

The tactical decision maker 221 takes control from step 238 of thestrategic decision making 223 by the strategic decision maker 220.

It analyses said data, identifies said typical cases, provides tacticaldecisions, which can be commented through the comment channel 131-133,and switches to diagnosing mode 247 in both passive and active manners,or to testing 248 or supply 249 modes in active manner of tutoring.

Concluding its operation, it transfer control to the operative decisionmaking 225 by the operative decision maker 222.

Table form of tactic decision making with examples of commenting isgiven in FIG. 47.

The Operative Decision Maker

Environment.

The operative decision maker 222 is a part of the decision maker 186.

Parameters

The operative decision maker 222 takes into account manner of tutoringand the learner personal data including requirements, preferences andthe type of tutoring assignments chosen by the learner (multiple,rating, or single assignment) through the control channel.

Optionally the operative decision maker 222 can take into accountauthor's opinions (script) on what to do next (when it is desirable tointegrate several sequencing mechanisms).

Function

It can provide the following different types of tutoring assignments:

-   -   a) a single tutoring assignment (i) to create target learning        situation (s) in the media environment 143 in order to initiate        desired learning activity of the learner;    -   b) a multiple tutoring assignments [i] representing a subset of        the whole set {i} of single tutoring assignments for a learner's        personal choice of just one single assignment (i);    -   c) a rating tutoring assignment Weight [i] representing said        multiple tutoring assignment [i] with single assignments rated        (with Weight) by the engine 181 in accordance with their        personal current utility for the learner.

Finally the operative decision maker 222 alone or in cooperation withthe learner provides the media-logic converter 142 with the singletutoring assignment (i′) to realize in the media environment 143 throughthe situation/response channel.

By default, the operative decision maker 222 provides only singletutoring assignments.

Composition.

The operative decision maker 222 includes the following modules 250-252connected in a sequence as it is shown in FIG. 48:

-   -   a) a sharp filter 250 generating said multiple tutoring        assignment [i] for the following manual choice by the learner or        automatic processing by the soft filter 251    -   b) a soft filter 251 generating said rating tutoring assignment        Weight [i] for a manual choice by the learner or automatic        selection by selector 252,    -   c) a selector 252 selecting the single tutoring assignment (i)        for the learner if the learner did not do it yet by him/herself.        Operation.

The operative decision maker 222 takes control from strategic decisionmaker 220 on step 223 and from tactic decision maker 221 on step 224.

In passive manner of tutoring, the operative decision maker 222transfers control to the executing step 131 for learning domain 160 andthe learner to act.

In active mode, depending of type of tutoring assignments chosen by thelearner, the operative decision maker 222 activates only the sharpfilter 250 for multiple assignments, or sharp 250 and soft 251 filtersfor rating assignments, or all three of them 250-251 for singleassignments. They operate sequentially beginning from the sharp filter250 taking into account learner requirements, through the soft filter251 taking into account learner's preferences and ending with theselector 252. The learner can make his/her own choice on each step ofthis process. The result of filtering are transferred for the executing131 to the controller 164. The final result of operative decision maker222 and the learner cooperation is always the single assignment (i′).More detail follows hereinafter.

The Sharp Filter

Environment.

The sharp filter 250 is a part of the operative decision maker 222.

Function.

The sharp filter 250 works in active manner of tutoring only. Itanalyses available tutoring assignments {i}, rejects inappropriatecandidates and by this way narrows a choice down to the multipleassignment [i] for the following soft filter 251 or the learner'sconsideration.

Input: The sharp filter 250 takes into account the following data:

-   -   a) Assignment properties including the implementation status,        IS(i), see FIG. 30;    -   b) State-behavior relation (may be pre-processed), see FIG. 31;    -   c) Learner requirements, see FIG. 32;    -   d) Learner state model, see FIGS. 34, 35;    -   e) Current tutoring mode: supply, testing, or diagnosing;    -   f) Current Difficulty limit, DL;    -   g) Current Testing Delay, TD.

Output: a subset [i] of the available set {i} of tutoring assignments.

Composition.

The sharp filter 250 includes eight rejecting rules 260-267 arranged intwo mode-dependent branches as it is shown in FIG. 49. The first rule260 is followed by linear sequence of rules 261-263 and a linearsequence of rules 264-267.

Operation.

The sharp filter 250 works in active tutoring manner only. The flowchartof its operation is illustrated in FIG. 49.

The operation is initiated from decision making 223 by strategicdecision maker 220 or from decision making 224 by tactic decision maker221 or from step 296 by reviser 189. Operating begins from the rule 260rejecting too difficult candidate assignments, which difficulty levelfrom assignment's data (see FIG. 30) exceeds the current difficultylimit (DL) of the learner from his/her learner model based on theframework 204;

Further operation is different for different tutoring modes (supply,testing and diagnosing).

In supply mode, the sharp filter considers all available assignments {i}(remaining after optional pre-processing) by default or only assignmentsspecifically prescribed for this mode by the author (which is optional,see FIG. 30) and performs the following sequence of the rules 261-263:

Rule 261: rejecting not-grounded candidate assignments, which aregrounded on at least one learning objective (j) in not yet suppliedachievement state. In quantitative form, this rule looks like: if anassignment (i) has corresponding supplying background beliefsSBB(i,s,j)>0 on at least one learning objective (j), for whichSAB(j)<ST, then this assignment (i) is definitely rejected. The optionalless restrictive form of this rule uses the condition SAB(j)=0.Actually, there is an optional possibility to customize rejecting powerof this rule by implementing a variable supply threshold (VST) forSAB(j) in a range: 0=<VST<ST.

Rule 262: rejecting overkill (too big for the learner) candidateassignments, which coverage of learning objectives that are not yet insaid supplied achievement state exceeds testing delay unit, TDL. Inquantitative form this rule is as follows: if in an assignment (i), sumof ILSB(i,s,j) for all objectives {j}, where SAB(j)<ST, is more thanTDL, then assignment (i) is rejected. The optional less restrictive formof this rule uses the condition SAB(j)=0. There is also all optionalpossibility to customize rejecting power of this rule by implementing avariable supply threshold (VST) for SAB(j) in a range: 0=<VST<ST.

Rule 263: rejecting excessive candidate assignments, which are able tosupply achievement of learning objectives only in already approvedsupplied achievement state. In quantitative form this rule looks like:if in an assignment (i), corresponding ILSB(i,s,j)>0 only on objectives,where SAB(j)>ST, then this assignment (i) is rejected. After completion,this rule transfers control to a supply sub-filter of the soft filter251.

In testing and diagnosing modes, the sharp filter considers allavailable assignments {i} (remaining after optional pre-processing) bydefault or only assignments specifically prescribed for these modes bythe author (which is optional, see FIG. 30) and performs the followinglearner sequence of the rules 264-267:

Rule 264 rejecting already implemented candidate assignments, which saidimplementation status has said “implemented” value, IS=1;

Rule 265 rejecting not-grounded candidate assignments, which aregrounded on at least one learning objective (j) in not yet demonstratedachievement state. In quantitative form, this rule looks like: if anassignment (i) has corresponding demonstrating background beliefsDBB(i,s,j)]>0 on at least one learning objective (j), for whichDAB(j)<TT, then this assignment (i) is rejected. The optional lessrestrictive form of this rule uses the condition DAB(j)=0. There is anoptional possibility to customize rejecting power of this rule byimplementing a variable testing threshold (VTT) for DAB(j) in a range:0=<VTT<TT. This rule is optional to provide a specific “bottom-up” orderof objective testing and diagnosing.

Rule 266 rejecting aside candidate assignments, which cover at least onelearning objective (j) that is not yet: in said supplied achievementstate. In quantitative form this rule looks as follows: if in anassignment (i) has ILDB(i,s,j)>0 on at least one learning objective (j)where SAB(j)<ST, then assignment (j) is rejected. The optional lessrestrictive form of this rule uses the condition SAB(j)=0. There is alsoan optional possibility to customize rejecting power of this rule byimplementing a variable supply threshold (VST) for SAB(j) in a range:0=<VST<ST.

Rule 267 rejecting excessive candidate assignments, which are able totest achievement of learning objectives only in already approveddemonstrated achievement state. In quantitative form this rule lookslike: if an assignment (i) has ILDB(i,s,j)>0 only on objectives whereDAB(j)>TT, then this assignment (i) is rejected. After completion, thisrule transfers control to testing and diagnosing soft-filters of thesoft filter 251.

The Soft Filter

Environment.

The soft filter 251 is a part of the operative decision maker 222.

Function.

It analyses assignment candidates [i] remained after the sharp filter250, rates them in accordance with their current utility for the learnerproviding the following selector 252 or the learner with a decisivebasis to select the best possible assignment.

Input: The soft filter takes into account the following data:

-   -   a) Assignment properties (see FIG. 30) including:    -   b) Properties mapping learner preferences,    -   c) Implementation status, IS(i).    -   d) type of tutoring assignment selected by the learner through        the control channel (multiple, rating, or single);    -   e) Current tutoring mode: supply, testing, or diagnosing;    -   i) Learner's preferences, see FIG. 33;    -   g) Current Difficulty limit of the learner, DL, from the        personal data framework 213;    -   h) State-behavior relation (may be pre-processed), see FIG. 31;    -   i) Learner state model, see FIGS. 34, 35;

Output: a rated Weight [i] subset [i] of available tutoring assignments{i}.

Composition

Soft filter 251 includes three separate sub-filters: a supplysoft-filter for supply mode, a testing soft-filter for testing mode, anddiagnosing soft-filter for diagnosing mode.

Operation.

In supply mode,

the supply soft-filter uses the following data:

-   -   a) expected progress provided by each candidate assignment (i)        and defined with integrated global supplying beliefs IGSB(i,s,j)        on learning objectives {j} in said no-achievement state defined        with said no-achievement beliefs NAB(j)>0;    -   b) expected progress provided by each candidate assignment (i)        and defined with integrated global supplying beliefs IGSB(i,s,j)        on learning objectives {j} in not yet supplied achievement state        defined with a complement to said supplied achievement beliefs        [1-SAB(j)]>0;    -   c) current prospect through learning objectives {j} provided by        previous assignments and quantitatively defined with P(j).    -   d) preferences of the learner, see FIG. 33;    -   e) Difficulty level DLE(i), see FIG. 30;    -   f) Implementation status, IS(i), see FIG. 30.

The supply soft-filter considers the following dependencies.

The more an assignment (i) can contribute to supplying no-achieved yetobjectives, the better. In other words, the more IGSB(i,s,j) falls intoNAB(j)>0, the more its weight should be. In simple preferred form, thisdependence can be represented by the following mathematical expression:${Weight}\quad(i)\quad{is}\quad{proportional}\quad{to}\quad{\sum\limits_{j}{{{IGSB}( {i,s,j} )}*{{{NAB}(j)}.}}}$

The more an assignment (i) can contribute to the learner's progressexpectation, the better. In other words, the more IGSB(i,s,j) falls intonot supplied yet objectives defined with [1-SAB(j)]>0, the more itsweight should be. In simple preferred form, this dependence can berepresented by the following mathematical expression:${Weight}\quad(i)\quad{is}\quad{proportional}\quad{to}{\sum\limits_{j}{{{IGSB}( {i,s,j} )}*{\lbrack {1 - {{SAB}(j)}} \rbrack.}}}$

The more an assignment (i) matches the prospect P(h) of learning supplyprovided by previous assignments, the more weight it should have. Thisrule prevents jumping aside from the current learning thread. In simplepreferred form, this dependence can be represented by the followingmathematical expression:${Weight}\quad(i)\quad{is}\quad{proportional}\quad{to}{\sum\limits_{j}{{{IGSB}( {i,s,j} )}*{{P(j)}.}}}$

The more an assignment properties Prop(i,q) match personal preferencesPref(q) of the learner the more its weight should be. In simplepreferred form, this dependence can be represented by the followingmathematical expression:${Weight}\quad(i)\quad{is}\quad{proportional}\quad{to}{\sum\limits_{q}{{{Prop}( {i,q} )}*{{{Pref}(q)}.}}}$

The higher difficulty level DLE(i) of an assignment within personalcurrent difficulty limit, DL, the better.

Weight (i) is proportionial to DLE(i).

Not yet implemented assignment is better, than already implemented.

Weight (i) is less for implemented assignments by implementation status,IS(i).

In quantitative form, these (generally conflicting) dependencies can becompromised by the following formula:${{ {{{Weight}\quad(i)} = {{{DLE}(i)}*{\sum\limits_{j}{{{IGSB}( {i,s,j} )}*\lbrack {1 - {{SAB}(j)} + {{NAB}(j)}} \rbrack*P(j)}}}} \}*{\sum\limits_{q}{{{Prop}( {i,q} )}**{{Pref}(q)}}}} - {{IS}(i)}};$which represents a simple preferred solution of the supply soft-filter.

This expression is open for further customizing and fine tuning.

In testing mode,

-   -   the testing soft-filter weights each assignment (i)        characterized by the ILDB(i,s,j) in accordance with its expected        coverage of learning objectives in the supplied achievement        state defined with SAB(j)>0 but not yet in said demonstrated        achievement state defined with a complement to DAB(j)>0.        Relevant dependencies look like follows:

The more the testing assignment (i) covers supplied learning objectivesdefined with SAB(j)>0, the better. In other words, the moreILDB(i,s,j)>0 covers SAB(j)>0, the more weight it should have.

The more the testing assignment (i) covers untested or ill-testedlearning objectives, the better. In other words, the more ILDB(j)>0covers [1-DAB(j)]>0, the more weight it should have.

The more the testing assignment (i) matches the prospect P(j) ofprevious supplying assignments, the more weight it should have. Thisdependency prevents jumping aside of testing thread, but is optional.

(4) The higher difficulty level, DLE(i), of an assignment withinpersonal current difficulty limit. DL, the better.

Weight (i) is proportional to DLE(i).

In quantitative form, these (in general, conflicting) rules can becompromised by the following formula:${{{Weight}\quad(i)}\quad = {{{DLE}(i)}*{\sum\limits_{j}{{{ILDB}( {i,s,j} )}*{{SAB}(j)}*\lbrack {1 - {{DAB}(j)}} \rbrack*P(j)}}}},$which represents a single preferred solution of the testing soft-filter.

This expression is open for further customizing and fine tuning).

In diagnosing mode,

-   -   the diagnosing soft-filter weights each assignment (i)        characterized at least by global demonstrating beliefs        GDB(i,s,k,j) and optionally with said global fault beliefs        GFB(i,s,k,j) in accordance with its ability to differentiate a        set of fault causing objectives defined with a fault cause        beliefs FCB(j) into more subsets of equal size. It is known from        Information Theory, that such method insures the most effective        diagnosing procedure.

The more the diagnosing assignment (i) is able to differentiateSuspected fault causes defined by FCB(j), the more weight it should have

In quantitative form, this dependency can be expressed by the followingformula, which represents a preferred solution of the diagnosingsoft-filter:${{{Weight}\quad(i)} = {\sum\limits_{q}{\sum\limits_{j}{\sum\limits_{h = {j + 1}}{{{{{MN}( {i,s,q,j} )} - {{MN}( {i,s,q,h} )}}}*{FCB}(j)*{{FCB}(h)}}}}}};$Where MN(i,s,q,j) and MN(i,s,q,h) represent pre-processed globaldemonstrating beliefs GDB(i,s,k,j) and global fault beliefsGFB(i,s,k,j). See FIGS. 38 and 39.

Note that if for some reasons, such as a customer's wish, it is desiredto use several sequencing engines in parallel, then their differentselections from the same set of possible assignments can be compromisedby the soft filter in the same manner.

Indeed, if each local engine provides its own subset of the same set {i}of assignments with local weight(i), then a compromise decision can bemade by any standard voting procedure, for example, by summing weight(i)from different engines for each (i).

The Selector

The Selector 252 is a part or operative decision maker 222. In preferredsimplest form, it selects the leading assignment candidate N withmaximal weight Weight[i], (if the learner did not do it yet):

-   -   i′=Argument Max Weight,    -   where [i] is a subset of initial set {i} of assignments        pre-selected by the sharp filter 250.

Other possible embodiments of the selector 252 can require a certaindegree of leadership (like leading by more than X-number of points) orcertain confidence in leadership (like confidence level should exceedcertain limit). However, in order to satisfy high requirements, thetutoring engine requires a larger pool of assignments, which design anddevelopment are labor consuming.

The Updater

Environment.

The updater 188 is a part of the data processor 187.

Parameters:

Functioning of the updater 188 is defined with the following parameters:

-   -   a) Tutoring learner (passive or active);    -   b) Tutoring mode (supply, testing, or diagnosing);    -   c) Customizable adaptation coefficients (INC and DEC) defining a        desired speed of adaptation process.        Function.

The updater 188 automates very complex “intelligent” function of humantutors “to under stand” what is going on with learning/tutoring of thelearner. To make it possible, it accepts learning reports (i′,s′,k′)from the step 133 performed by the monitor 165, interprets them intosaid learning state space model using said state-behavior relation, andupdates current beliefs of the learner state model.

Initial data (in case of the first use of the instructional unit by thelearner) include:

-   -   a) no-achievement beliefs NAB(j)=0;    -   b) supplied achievement beliefs SAB(j)=0;    -   c) demonstrated achievement beliefs DAB(j)=0;    -   d) the tutoring prospect P(j)=0;    -   e) the difficulty limit (DL) from the learner personal data.        Default DL=2;    -   f) the testing delay limit (TDL) from the learner personal data.        Default TDL=3,    -   g) the fault tolerance limit FTL from the learner personal data.        Default value is one (0.3):    -   h) FCB(j)=NAB(j).        Input:    -   a),earning behavior report including        -   1. assignment identifier (i′),        -   2. situation identifier (s′) and        -   3. response identifier (k′);    -   b) Beliefs of the state-behavior relation: LDB(i,s,k,j),        LSB(i,s,k,j) and LFB(i,s,k,j), may be pre-processed;

Outcome:

-   -   a) Current beliefs of the learner state model: DAB(j), SAB(j),        NAB(j), P(j);    -   b) current difficulty limit (DL);    -   c) current testing delay limit (TDL).        Composition

The updater 188 comprises eight updating rules 281-288. Rules 281-283and 286-288 are arranged in a linear order. The gap between rules 283and 286 is filled with rule 284 in case of passive diagnosing mode, andwith rule 285 in case of active diagnosing mode. The composition of theupdater is illustrated in FIG. 50.

Operating.

Operating of the updater 188 is initiated with the learning report (i′,s′,k′) from the step 133 performed by the monitor 165.

In both passive and active tutoring manners, the updater accepts thelearning report (i′,s′,k′) from the monitor 165, then it retrieves acorresponding part of state-behavior relation and uses these data toupdate current beliefs of the learner state model. An entire updatingprocedure includes the following steps executed by corresponding rules:

Rule 281: said demonstrated achievement beliefs DAB(j) from the learningstate model is combined with the local demonstrating beliefsIDB(i′,s′,k′,j) from the part of the state-behavior relationcorresponding to the tutoring assignment (i′), identified situation (s′)and response (k′) from said learning report and considered as the DAB(j)again. In case of unexpected response identified with k′=K+1,IDB(i′,s′,k′=K+1,j)=0. In quantitative preferred form, this steprepresents the following iteration:DAB(j)←DAB(j)+LDB(i′,s′,k′,j)−DAB(j)*LDB(i′,s′,k′,j).

Rule 282: said supplied achievement belief SAB(j) from the learningstate model is combined with the local supplying belief LSB(i′,s′,k′,j)from the part of the state-behavior relation corresponding to thetutoring assignment (i′), identified situation (s′) and response (k′)from said learning report. Then the result of combining is compared withthe DAB(i) and the highest value is considered as the SAB(j) again. Incase of unexpected response identified with k′=K+1,LSB(i′,s′,k′=K+1,j)=0.

In quantitative preferred form, this step looks like the followingiteration step:SAB(j)←Max{DAB(j), [SAB(j)+LSB(i′,s′,k′,j)−SAB(j)*LSB(i′,s′,k′,j)]}.

Rule 283: said no-achievement belief NAB(j) from the learning statemodel is combined with the global fault belief GFB(i′,s′,k′,j)representing the preprocessed part of the state-behavior relationcorresponding to the tutoring assignment (i′), identified situation (s′)and response (k′) from said learning report. Then the result ofcombining is compared with a complement to the DAB(j) and the lowestvalue is considered as the NAB(j) again. In case of unexpected responseidentified with k′=K+1, said global fault beliefsGFB(i′,s′,k′=K+1,j)=IGDB(i′,s′₄).

In quantitative preferred form, this step looks like the followingiteration:NAB(j)←Min{[1−DAB(j)], [NAB(j)+GFB(i′,s′,k′,j)−NAB(j)*GFB(i′,s′,k′,j)]}.

Rule 284: in case of said diagnosing mode of passive tutoring manner,said fault cause beliefs FCB(j), which prior to said diagnosing modewere equal to the no-achievement beliefs NAB(j) are summed with saidglobal fault beliefs GFB(i′,s′,k′,j) from the preprocessed part of thestate-behavior relation corresponding to the tutoring assignment (i′),identified situation (s′) and response (k′) from said learning report.Then the sum is compared with a complement: to the DAB(j) and the lowestvalue is considered as the FCB(j) again. In case of unexpected responseidentified with k′=K+1, GFB(i′,s′,k′=K+1,j)=IGDB(i′,s′,j).

In quantitative preferred form, this step looks like the followingiteration:FCB(j)←Min{[1−DAB(j)], [FCB(j)+GFB(i′,s′,k′,j)]}.

Rule 285: in case of said diagnosing mode of active tutoring manner,said fault cause beliefs FCB(j), which prior to said diagnosing modewere equal to the no-achievement beliefs NAB(j), are intersected withsaid global fault beliefs GFB(i′,s′,k′,j) from the preprocessed part ofthe state-behavior relation corresponding to the tutoring assignment(i′), identified situation (s′) and response (k′) from said learningreport. Then the result of intersecting is compared with a complement tothe DAB(j) and the lowest value is considered as the FCB(j) again. Incase of unexpected response identified with k′=K+1,GFB(i′,s′,k′=K+1,j)=IGDB(i,s,j).

In quantitative preferred form, this step looks like the followingiteration:FCB(j)←Min{[1−DAB(j)],FCB(j)*GFB(i′,s′,k′,j)}.

Rule 286: said tutoring prospect P(j) from the learning state model iscombined with global supplying beliefs GSB(i′,s′,k′,j) from thepreprocessed part of the state-behavior relation corresponding to thetutoring assignment (i′), identified situation (s′) and response (k′)from said learning report and considered as the said tutoring prospectP(j) again. In case of unexpected response identified with k′=K+1, saidglobal supplying beliefs GSB(i′,s′,k′=K+1,j)=0. In order to emphasizethe last supply, this combination should take into account the latestvalues of GSB(i′,s′,k′,j) with higher weight and gradually fade off oldones. In preferred simple embodiment, a quantitative form of this rulelooks like the following iteration:P(j)←[P(j)+GSB(i′,s′,k′,j)]/2;

-   -   Rule 287: incrementing the current value of personal difficulty        limit DL in accordance with a last increment of DAB(j) and        decrementing said DL in accordance with a last increment of        NAB(j). In preferred simple embodiment, a quantitative form of        this rule looks like the following iteration:        $ {DL}arrow{{Max}\{ {1,{{DL} + {{INC}*{\sum\limits_{j}\lbrack {{{DAB}(j)} - {{DAB}(j)}^{\prime}} \rbrack}} - {{DEC}*{\sum\limits_{j}\lbrack {{{NAB}(j)} - {{NAB}(j)}^{\prime}} \rbrack}}}} \}} ,$

Where:

-   -   DL is automatically kept>=1;    -   INC is an incrementing coefficient;    -   DEC is a decrementing coefficient. Recommended INC=DEC=1/J:    -   J is a number of learning objectives in an instructional unit;    -   DAB(j)′ and NAB(j)′ are corresponding DAB(j) and NAB(j) from the        previous cycle of updating.

Rule 288: incrementing the current value of testing delay limit TDL inaccordance with the last increment of DAB(j) and decrementing said TD inaccordance with the last increment of NAB(j). In preferred simpleembodiment, a quantitative form of this rule looks like the followingiteration:$ {TDL}arrow{{Max}\{ {1,{{TDL} + {{INC}*{\sum\limits_{j}\lbrack {{{DAB}(j)} - {{DAB}(j)}^{\prime}} \rbrack}} - {{DEC}*{\sum\limits_{j}\lbrack {{{NAB}(j)} - {{NAB}(j)}^{\prime}} \rbrack}}}} \}} ,$

Where:

-   -   TDL is automatically kept>=1;    -   INC is an increment coefficient;    -   DEC is a decrement coefficient. Recommended INC=DEC═1/J;    -   J is a number of learning objectives in an instructional unit;    -   DAB(j)′ and NAB(j)′ are corresponding DAB(j) and NAB(j) from the        previous cycle of updating.

After completion, the rule 288 transfers control to the step 230 ofdecision making 223 performed by the strategic decision maker 220.

Uncertain Identification of Behavior

Sometimes, the monitor 165 cannot identify the learning behavior (i,s,k)exactly but with uncertainty.

In this generic case, the monitor 165 can provide the tutoring generator141 wraith behavior reports which instead of just (k′) includes beliefsRB(k) defining likelihood of actual response of the learner to eachexpected response (k) from said plurality of expected responses (k=1,2,. . .K) plus one unexpected response (K+1).

Actually, the same is fair for situation (s) identification. But intutoring practice, the learning situation(s) can be determined byassigning specific learning resource (r) which is a common practice,while response (k) cannot be determined because of unpredictability, ofthe learner. That is why the most practical interest represents behaviorreports such as (i′, s′, RB(k)).

In this case, described updating method realized by the updater 188 canbe performed separately for each response (k), for which correspondingRB(k)>0 as it has been described above. Then each separate resultsDAB(j,k), SAB(j,k), NAB(j,k), FCB(j,k), and P(j,k) depending of (k)should be integrated together by calculating their Mean value across all{k} with corresponding weight of RB(k):

-   -   In rule 281, the DAB(j) in right side of equation should be        replaced with        ${{{DAB}(j)} = {\sum\limits_{k = 1}^{K + 1}{{{DAB}( {j,k} )}*{{{RB}(k)}/( {1 + K} )}}}};$    -   In rule 282, the SAB(j) in right side of equation should be        replaced with        ${{{SAB}(j)} = {\sum\limits_{k = 1}^{K + 1}{{{SAB}( {j,k} )}*{{{RB}(k)}/( {1 + K} )}}}};$    -   In rule 283, the NAB(j) in right side of equation should be        replaced with        ${{{NAB}(j)} = {\sum\limits_{k = 1}^{K + 1}{{{NAB}( {j,k} )}*{{{RB}(k)}/( {1 + K} )}}}};$    -   In rule 284, 285, the FCB(j) in right side of equation should be        replaced with        ${{{FCB}(j)} = {\sum\limits_{k = 1}^{K + 1}{{{FCB}( {j,k} )}*{{{RB}(k)}/( {1 + K} )}}}};$    -   In rule 286, the P(j) in right side of equation should be        replaced with        ${{P(j)} = {\sum\limits_{k = 1}^{K + 1}{{P( {j,k} )}*{{{RB}(k)}/( {1 + K} )}}}};$    -   Described use of learning reports with uncertainty (i′, s′,        RB(k)) can be easily extended up to (i′, SB(s), RB(k)) or even        (AB(i), SB(s), RB(k)), where SB(s) and AB(i) denotes        correspondingly situational beliefs and assignment beliefs.        The Reviser        Environment.

The reviser 189 is a part of the data processor 187.

Function.

The reviser 189 revises the learner state model, if the approvedno-achievement state (diagnosis) is identified for a learning objective.

Input:

-   -   a) supplied achievement beliefs SAB(j);    -   b) demonstrated achievement beliefs DAB(j);    -   c) tutoring prospect P(j);    -   d) global succeed beliefs GSCB(j,h);    -   e) personal difficulty limit, DL;    -   f) personal testing delay limit, TDL,

Outcome:

-   -   a) revised learner state model;    -   b) personal difficulty limit, DL;    -   c) personal testing delay limit, TDL,        Composition.

The reviser 189 comprises five revising rules 291-295 and a mode switch296 arranged in linear order. See FIG. 51.

Operation.

Operating the reviser 189 starts from decision making 223 performed bythe strategic decision maker 220 and represents a linear step by stepexecution of the rules 291-295 and switch 296 as it illustrated in FIG.51.

Rule 291: setting up said supplied achievement belief SAB(j′) anddemonstrated achievement belief DAB(j′) of the diagnosed objective (j′)to zero, SAB(j′)=DAB(j′)=0;

Rule 292: revising said supplied achievement belief SAB(j) anddemonstrated achievement belief DAB(j) of all other (no j′) learningobjectives {j} by their intersecting with a complement to the globalsucceed beliefs GSCB(j,j′) and considering result as said suppliedachievement belief SAB(j) and demonstrated achievement belief DAB(j)again. In simple preferred form, it can be done by the followingoperations:SAB(j)←SAB(j)*[1−GSCB(j,j′)],DAB(j)←DAB(j)*[1−GSCB(j,j′)].

Rule 293: setting tip said tutoring prospect P(j) to start from thediagnosed objective (j′) by setting the h=j′ in said global succeedbeliefs GSCB(j,h=j′) and considering it as a tutoring prospectP(j)=GSCB(j,h=j′);

Rule 294: setting up said difficulty limit DL to its minimum value,DL=1;

Rule 295: setting up said testing delay limit TDL to its minimum value,TDL=1.

Setting up the supply mode of active tutoring by the switch 296.Completing this rule initiates the step 250 of decision making 225 bythe tactic decision maker 222.

Evaluating the Instructional Unit

Collecting personal learning histories provides an opportunity toanalyze them and evaluate general efficiency of the instructional unit.The methods of general evaluating are known as summative evaluation.Analysis allows also detecting common learning problems, backtrackingtheir possible causes and revealing what exactly to improve in theinstructional unit. It is a formative evaluation. Both represent theoptional evaluating 106 step of the tutoring method as shown in FIG. 2.

In addition to known summative, the formative evaluating 106 of theinstructional unit may include the following steps:

-   -   a) accumulating problematic objective beliefs POB(j) of the        learner in the instructional unit. POB(j) can be expressed, for        example, by said fault cause beliefs FCB(j) or by number of        diagnosis made per objective. It can be done, for example, by        summing said fault cause beliefs FCB(j) in each updating cycle        of the updater 188 with said POB(j) or by counting number of        diagnosis made per objective (j) within each instructional unit.        The latter is a preferred solution;    -   b) accumulating the personal problematic objective beliefs        POB(j) across the entire audience. It can be done, for example,        by summing the personal said problem objective beliefs POB(j) or        by summing personal number of diagnosis made per objective (j)        for all learners from the target audience;    -   c) Inference of problematic assignment beliefs PAB(i,s) for each        assignment (i) and learning situation (s). It can be done by        standard operation of linear production of said problematic        objective beliefs POB(j) with the integrated local supplying        beliefs ILSB(i,s,j) and the integrated local demonstrating        beliefs ILDB(i,s,j):        -   1. Problematic assignment beliefs for supply PABS(i,s)=            ${\sum\limits_{j = 1}^{J}{{{POB}(j)}*{{ILSB}( {i,s,j} )}}};$        -   2. Problematic assignment beliefs for testing PABT(i,s)=            ${\sum\limits_{j = 1}^{J}{{{POB}(j)}*{{ILDB}( {i,s,j} )}}};$    -   d) Providing authors with advices to fix specific tutoring        assignments [i] and specific learning situations [s] according        to the value of said problematic assignment beliefs for supply        PABS(i,s) and for testing PABS(i,s). The assignment with the        maximal value is advised to be fixed first.        Composition.

Evaluating 106 is performed by the improver 191 including

-   -   a) Means to accumulate said personal and audience problematic        objective beliefs POB(j) during learning process within each        specific instructional unit.    -   b) Means to perform inference of problem assignment beliefs        PABS(i,s) and PABT(i,s) for the tutoring report extended with        these data by demand;    -   c) Means to provide advises to the authors in an appropriate        media form.

After this evaluation 106, the following improving 107 step performed byauthors manually is supposed to improve the media 143 and the logic 184,which include beliefs LSB(i,s,k,j) and LDB(i,s,k,j).

Automatic Improving the Logic of the Instructional Unit

During normal course of operating with learners from the targetaudience, the generator 141 is able to improve its specificknowledge/data 184 within the instructional unlit by automaticperforming the optional steps 106-107 of the outer tutoring loop asillustrated in FIG. 2.

The automatic improving is based on the following generic rules:

Rule A: if achievement of certain objective was successful, then it israther due to the fact that

-   -   a) tutoring supply was proper;    -   b) testing of used background was proper.    -   c) Thus implemented supply and demonstrating beliefs can be        incremented;

Rule B: if learning was unsuccessful, but diagnosed, re-supplied andtested successfully, then it is rather due to the fact that

-   -   a) tutoring supply of diagnosed objective was improper;    -   b) diagnosis was proper;    -   c) Thus supply beliefs implemented for diagnosed objective can        be decremented and    -   d) fault beliefs implemented for the correct diagnosis can be        incremented,

Rule C: if learning was unsuccessful, and diagnosed, re-supplied andtested unsuccessfully again, then it is rather due to the fact thatdiagnosis was incorrect.

-   -   a) Thus implemented for the incorrect diagnosis fault beliefs        can be decrement.

Automatic evaluating 106 and improving 107 extends the whole operationalcycle of the tutoring generator 141 with the couple of outer steps. Theautomatically performed steps 106-107 can be aggregated in one step 217of the generator operating and as it is demonstrated in FIG. 41 insertedbetween updating 215 and decision making 130 steps.

Automatic evaluating/improving 217 include the following steps:

In the beginning of each tutoring session, initializing the followingmemory registers:

-   -   a) Current supply register=empty;    -   b) Previous supply register=empty;    -   c) Pre-previous supply register=empty;    -   d) Current testing register=empty;    -   e) Previous testing register=empty;    -   f) Current diagnosing register=empty;    -   g) Previous diagnosing register=empty;    -   h) Previous DAB(j)′=0.

In normal course of tutoring 105, the improver 191 stores identifiers(i′,s′,k′) of implemented assignments, realized situations, andrecognized responses in 3 following memory registers accordingly to thecurrent mode:

-   -   a) Current supply register in supply mode;    -   b) Current testing register in testing mode.    -   c) Current diagnosing register in diagnosing mode;

In normal course of generator 141 operating, changing the current modeinitiates the following operations:

-   -   a) if supply mode is stopped, then        -   1. Previous supply register←Current supply register.        -   2. Pre-previous supply register←Previous supply register;    -   b) if testing mode is stopped, then Previous testing        register←Current testing resgister    -   c) if diagnosing mode is stopped, then Previous diagnosing        register←Current diagnosing register;    -   d) Previous DAB(j)′←current DAB(j);

In normal course of generator 141 operating, checking: If tutoring wassuccessful, which means that during testing/diagnosing mode, there is anobjective (j′), for which [DAB(j′)−DAB(j′)′]>TT, then for this objective(j′):

-   -   a) incrementing LSB(i′,s′,k′,j′) and GSB(i′,s′,k′,j′) of all        assignments/situations/responses (i′,s′,k′) from the previous        supply register that properly supplied this achievement of this        objective (j′):        -   1.            LSB(i′,s′,k′,j′)←LSB(i′,s′,k′,j′)+SPD*[1−LSB(i′,s′,k′,j′)];        -   2.            GSB(i′,s′,k′,j′)←GSB(i′,s′,k′,j′)+SPD*[1−GSB(i′,s′,k′,j′)];    -   b) incrementing LDB(i′,s′,k′ j) and GDB(i′,s′,k′,j) of all        assignments/situations/responses (i′,s′,k′) from the previous        testing register that properly confirmed the background        GPRB(j,j′)>0 of this objective (j′):        -   1. For all (j) where GPRB(j,j′)>0 do:        -   2. LDB(i′,s′,k′,j)←LDB(i′,s′,k′,j)+SPD[1−LSB(i′,s′,k′,j)];        -   3. GDB(i′,s′,k′,j)←GDB(i′,s′,k′,j)+SPD*[1−GSB(i′,s′,k′,j)];

In normal course of generator 141 operating, detecting if a diagnosishas been posed.

In normal course of generator 141 operating, specifically afterdiagnosing of objective (j′), revising 216, re-supplying and testingpositively [DAB(j′)−DAB(j′)′]>TT,

-   -   a) incrementing GFB(i′,s′,k′,j′) of all        assignments/situations/responses (i′,s′,k′) from the previous        diagnosing register that correctly suspected this objective        (j′):        -   1.            GFB(i′,s′,k′,j′)←LSB(i′,s′,k′,j′)−SPD*[1−GFB(i′,s′,k′,j′)];    -   b) decrementing LSB(i′,s′,k′,j′) and GSB(i′,s′,k′,j′) of all        assignments/situations/responses (i′,s′,k′) from the        pre-previous supply register that failed to supply this        objective (j′):        -   1. ISB(i′,s′,k′,j′)-77            (LSB(i′,s′,k′,j′)−SPD[1−LSB(i′,s′,k′,j′)];        -   2.            GSB(i′,s′,k′,j′)←GSB(i′,s′,k′,j′)−SPD*[1−GSB(i′,s′,k′,j′)];    -   c) decrementing LDB(i′,s′,k′,j′) and GDB(i′,s′,k′,j′) of all        assignments/situations/responses (i′,s′,k′) from the previous        testing register that improperly confirmed achievement of this        objective (j′) prior to the current testing:        -   1.            LDB(i′,s′,k′,j′)←(LDB(i′,s′,k′,j′)−SPD*[1−LSB(i′,s′,k′,j′)];        -   2.            GDB(i′,s′,k′,j′)←GDB(i′,s′,k′,j′)−SPD*[1−GSB(i′,s′,k′,j′)];

In normal course of generator 141 operating, specifically afterdiagnosing of objective (j′), revising 216, supplying and testingnegatively [DAB(j′)-DAB(j′)′]<1−TT.

-   -   a) decrementing GFB(i′,s′,k′,j′) of all        assignments/situations/responses (i′,s′,k′) from the previous        diagnosing register that may be incorrectly suspected this        objective (j′);    -   b) GFB(i′,s′,k′,j′)←GFB(i′,s′,k′,j′)−SPD[1−GFB(i′,s′,k′,j′)];

Where

SPD is an adjustable speed of improvement with a range 0=<SPD=<1 andrecommended default value SPD=0,01;

Composition.

The described method is performed by the improver 191 which has memory182 registers:

-   -   a) Current supply register;    -   b) Previous supply register;    -   c) Pre-previous supply register;    -   d) Current testing register;    -   e) Previous testing register;    -   f) Current diagnosing register;    -   g) Previous diagnosing register;    -   h) Previous DAB(i′,s′,k′,j) register;    -   i) and a processor for performing described operations.

Note that this automatic improvement is supposed to change only logicnot media of leaning resources.

In principle, the described procedure of self-improvement can be used inorder to develop the logic by demonstration, not by its description evenin such simplified form as filling in the frameworks. But it takes longtime. That is why a preferred solution begins from prior manualauthoring followed by the automatic self-improvement.

In Depth Description of the Tutoring Generator Operating

Now, after describing all details of the tutoring system 140 and thetutoring method 105 it is possible to detail the whole operating of thetutoring generator 141 (as it was illustrated in FIG. 22) in finestgrains.

In passive manner, in each cycle of the tutoring, the tutoring generator141 performs the following cycle of operations:

-   -   a) making 223 decisions by the strategic decision maker 220        (accompanied with corresponding comment messages through the        comment channel).        -   1. Particularly, the rule 233 decides: If the approved            demonstrated achievement state is identified for all            (terminal) objectives {j}, then praise the learner, provide            a summary, assign 239 reporter 190 to generate the tutoring            report and end tutoring.        -   2. The rule 237 decides: if the approved no-achievement            state of one of said plurality of learning objectives is            identified (diagnosis), then commenting this case and            advising the learner to switch to active manner for remedy            diagnosed learning problem;    -   b) making 224 limited tactic decisions 242-247 by the tactic        decision maker 221.        -   1. Particularly, rule 242 decides: if sum of no-achievement            beliefs NAB(j) ir all learning objectives {j} exceeds said            fault tolerance limit (FTL), then it begins passive            diagnosing mode by focusing its beliefs updating 284 oil a            cause of detected faults starting from setting up the fault            cause beliefs FCB(j) equal to current no-achievement beliefs            NAB(j), FCB(j)=NAB(j);    -   c) obtaining said learning behavior report (i′,s′,k′) by the        updater 188 from the monitor 165;    -   d) updating 281-288 said learner state model and personal data        by the updater 188;    -   e) making 223 new tutoring decisions by the strategic decision        maker 220.

In active tutoring manner, which can be administratively assignedmanually by an administrator/instructor/learner or automaticallyselected by the tutoring generator 141 being in passive manner, thetutoring generator 141 dynamically switches 240, 241, 247-249 thecurrent tutoring mode from the plurality of available (supply, testingand diagnosing) modes. Then within each mode it dynamically selects260-267 multiple assignment [i] by sharp filter 250, rated assignmentWeight[i] by soft filter 251 or single assignment by selector 252 forthe learner by performing the following cycle of operations:

-   -   a) making 223 (including steps 230-241) decisions by the        strategic decision maker 220.        -   1. Particularly, the rule 233 decides: If the approved            demonstrated achievement state is identified for all            (terminal) objectives {j}, then praise the learner, provide            a summary, assign 239 the reporter 190 to generate the            tutoring report and end tutoring.    -   b) making 224 (including steps 242-249) decision by the tactic        decision maker 221;    -   c) making 225 (including 250-252) decision by the operative        decision maker 222;    -   d) obtaining the learning behavior report (i′,s′,k′) by the        updater 188 from the monitor 165;    -   e) optional evaluating/improving 217 knowledge/data 184 by the        improver 191;    -   f) updating 281-288 the knowledge/data 184 by the updater 188;    -   g) making 223 (including steps 230-241) new decisions by the        strategic decision maker 220.        The Big Picture of the Logic Generator Implementation

The big picture of the generator 141 implementation in tutoring design100 and implementing 105 looks as follows:

-   -   a) Instructional unit design 100:        -   1. Designing the logical learning space of the instructional            unit by filling in the specific domain/task-specific data            184 into the uniform reusable framework 203;        -   2. Automatic verification of entered logical data for            consistency and sufficiency as it was described            hereinbefore;        -   3. Running the instructional unit by the tutoring engine 181            in provided logical learning space for its testing            (evaluating 106) and debugging (improving 107) purposes            prior to investing in developing any media yet. A reusable            fake learning environment 143 and converter 142 should be            constructed in advance in order to support this logical            operation.        -   4. Collecting 101 available and/or developing new media            learning resources and their playback tools to realize            desired learning situations to support desired learning            activities;        -   5. Assembling 104 a complete instructional unit including            created logic (the learning space) and media (learning            resources and tools);        -   6. Optional publishing developed instructional unit for use            in available administrative/management systems;        -   7. Optional but recommended design of the learning model for            each learner from the target audience by filling in the            learner data framework 204 with personal requirements and            preferences;    -   b) Optional administering:        -   1. Identifying a specific instructional unit (u);        -   2. Identifying the learner (l) and corresponding learner            model;        -   3. Providing tutoring generator 141 with the administrative            assignment;    -   c) Tutoring session 105:        -   1. unit data initialization and optional pre-processing by            the tutoring engine 181;        -   2. conducting a learning session by the entire tutoring            system 140;        -   3. providing tutoring report to an administrative level;

Described big picture explains developing new instructional units fromscratch. Available instructional units can be upgraded as well byrevealing a hidden logic behind available multimedia learning resourcesin order to fill in provided logical frameworks.

The foregoing disclosure has been set forth merely to illustrate theinvention and is not intended to be limiting. Since modifications of thedisclosed embodiments incorporating the spirit and substance of theinvention may occur to persons skilled in the art, the invention shouldbe construed to include everything within the scope of the appendedclaims and equivalents thereof.

REFERENCES

-   Bloom, B. S. (1984) The 2 sigma problem: The search for methods of    group instruction as effective as one-to-one tutoring. Educational    Researcher, 13 (6):4-16, 1984.-   Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R.    (1995). Cognitive Tutors: Lessons learned. The Journal of the    learning Sciences, 4, 167-207.-   Mislevy, R. J., & Gitomer, D. H. (1996). The role of    probability-based inference in an intelligent tutoring system. User    Modeling and User-Adapted Interaction 5 (3-4).-   Goodkovsky, V. A. (1992). Intelligent Tutoring Systems: Theory,    Technology, and Practice. In Proceeding of International “East-West”    Conference on Emerging Computer Technologies in Education. Moscow,    ICSTI, 1992.-   Goodkovsky, V. A. (1993). Intelligent Tutoring Systems. From theory    to practice. In Proceedings of the East-West conference on    Artificial Intelligence (pp. 305-309). Moscow, Russia. 1993.-   Goodkovsky, V. A. (1993). Practical Knowledge Diagnostics.    Theoretical Systems Approach. In Proceedings of the International    Conference on Computer Technologies in Education. (pp. 141-143).    Kiev, Ukraine, 1993.-   Goodkovsky, V. A. (1994). Intelligent Tutoring System: Theoretical    Systems Approach. In Proceedings of Japan—CIS Symposium on Knowledge    Based Software Engineering. (106-109). Pereslavl-Zalesskiy, Russia,    1994-   Goodkovsky, V. A. (1997). “Intelligent Tutor”: Top-down Approach to    Intelligent Tutoring System Design. Learning Technology Standards    Committee (P1484)—Developing Technical Standards for Learning    Technology.    http://ltsc.ieee.org/archive/harvested-2003-10/miscellaneous/goodkov/goodkov.htm-   Goodkovsky, V. A. (1997). Pop Class Intelligent Tutoring Systems:    Shell, Toolkit & Design Technology. In book “New Media and Telematic    technologies for Education in Eastern European Countries”. pp.    179-192, The Netherlands, Twente University Press, 1997.-   Goodkovsky, V. A. (2000). Intelligent Tutoring System. U.S. Patent    Application #20020107681, Kind A1, Aug. 8, 2002.-   Woolf, B. P., Beck, J., Eliot, C., & Stern, M. (2001). Growth and    maturity of intelligent tutoring systems: A status report, In K. D.    Forbus & P. J. Feltovich (Eds.), Smart machines in education (pp.    100-144). Cambridge, Mass.: MIT Press.-   Graesser A. C., Person, N. K., & Harter, D. (2001). Teaching tactics    and dialog in autotutor. International Journal of Artificial    Intelligence in Education, 12, 12-23.-   Richard Stottler & Nancy Harmon (2003). An Intelligent Tutoring    System (ITS) for Battlespace Geometry Tutoring.    Interservice/Industry Training, Simulation, and Education Conference    (I/ITSEC), 2003.-   Bruce Mills. (2002) Using the Atlas Planning Engine to Drive an    Intelligent Tutoring System: CIRCSIM-Tutor. Version 3. Proc. of the    Fourteenth International Florida Artificial Intelligence Research    Soc. Conf., Key West, Fla., May 2001, pp. 211-215.-   Rob Hubal & Curry Guinn. A Mixed-Initiative Intelligent Tutoring    Agent for Interaction Training.-   Valerie Shute, et al. (1997). Automating Cognitive Task Analysis.    Cognitive Technologies for Knowledge assessment symposium. AERA,    Chicago, Ill., 1997.-   Joseph M. Scandura (2003). Domain Specific Structural Analysis for    Intelligent Tutoring Systems: Automatable Representation of    Declarative, Procedural and Model-Based Knowledge with Relationship    to Software Engineering. Tech., Inst., Cognition and Learning. Vol.    1, pp. 7-57.Old City Publishing Inc. 2003.-   Brian P. Butz. Freedom of Choice in an Intelligent Tutoring    System*Session 3630. Electrical and Computer Engineering Department.    Temple University, Philadelphia, Pa. 19122-   R. Charles Murray and Kurt VanLehn (2000). DT tutor: A    decision-Theoretic, Dynamic Approach for Optimal selection of    Tutorial Actions. In G. Gauthier, C. Frasson, and VanLehn (Ed.),    Intelligent Tutoring systems, 5th International Conference, ITS    2000, pp. 153-162. New York: Springer.-   A. Patel et al. An initial framework of contexts for designing    usable intelligent tutoring systems. The contexts of intelligent    tutoring systems 1.-   Ashok Patel and Kinshluik. KNOWLEDGE CHARACTERISTICS: RECONSIDERING    THE DESIGN OF INTELLIGENT TUTORING SYSTEMS.-   Babbitt, et al. (2000). Intelligent flight tutoring system. U.S.    Pat. No. 6,053,737 Apr. 25, 2000.-   Sun-Teck Tan (1996) Architecture of a generic instructional planner.    In Journal of Network and Computer Applications, 1996, 19, 265-274.-   Jens O. Liegle and Han-Gyun Woo. Developing Adaptive Intelligent    Tutoring Systems: A General Framework and Its Implementations.-   Eman El-Sheikh and Jon Sticken. (1998). A framework for Developing    Intelligent Tutoring Systems Incorporating Reusability. IEA-195-AIE:    11th International Conference on Industrial and Engineering    Applications of Artificial Intelligence and Expert Systems,    Benicassim, Catellon, Spain, Springer-Verlag (Lecture Notes in    Artificial Intelligence, vol. 1415).-   Dietrich Albert Cord Hockemeyer. (1997). Adaptive and Dynamic    Hypertext tutoring Systems Based oil Knowledge Space Theory.    http://wundt.kfunigraz.ac.at/rath/publications/aied-97/aied-97.html-   Brusilovsky Peter (2003). Adaptive Navigation Support in Educational    Hypermedia: The Role of Student Knowledge Level and the Case for    Meta-Adaptation British Journal of Educational Technology, 34 (4),    486-497, 2003.

1. a method of tutoring a learner including: a) Providing a tutoringsystem including 1) providing a media environment for physicalsupporting at least one learning activity of said learner; 2) providinga unified tutoring logic generator for making a plurality of tutoringdecisions; 3) providing a media-logic converter a. for executing saidtutoring decisions in said media environment to support said learningactivity of said learner and b. for providing said logic generator withat least one report about said learning activity in said mediaenvironment; 4) associating said logic generator with said mediaenvironment by said media-logic converter: b) tutoring the learner withsaid tutoring system by controlling over said learning activity of saidlearner in said media environment with said logic generator through saidlogic-media converter whereby said method completely separates media andlogic of tutoring, enables unified logic-based generating of a specificmedia-dependent tutoring process, simplifying authoring, improvingquality of the tutoring process and accelerating learning success;
 2. amethod as in claim 1, wherein said providing a logic generator formaking a plurality of tutoring decisions including a) providing aunified knowledge/data model referenced to said learner and said leaningactivity including 1) providing a memory for storing knowledge/data; 2)providing a unified reusable knowledge/data framework for representingspecific knowledge/data in said memory; 3) providing said unifiedreusable knowledge/data framework with said specific knowledge/data; b)providing a unified reusable tutoring engine including 1) providing adecision maker for making a plurality of tutoring decisions based uponsaid knowledge/data model; 2) providing a processor for adapting saidknowledge/data model based upon at least one said learning, report aboutat least one said learning activity of at least one said learner: c)associating said knowledge/data model with said tutoring engine; wherebysaid method provides unified reusable components for building anyspecific tutoring system, excludes manual design of the tutoring processby authors, improves quality of said tutoring process and accelerateslearning success;
 3. a method as in claim 1, wherein said tutoring thelearner with said tutoring system including a) making tutoring decisionsfrom said plurality of tutoring decisions by said decision maker basedupon said unified knowledge/data model; b) executing said tutoringdecisions by said media-logic converter providing necessary control oversaid learning media environment; c) supporting said learning activity ofsaid learner by said media environment; d) monitoring said learningactivity and providing, said logic generator with at least one saidreport by said media-logic converter; e) adapting said unifiedknowledge/data model by said processor including particularly updatingsaid knowledge/data model based upon said report; f) making new tutoringdecisions from said plurality of tutoring decisions by said decisionmaker based upon adapted unified knowledge/data model; whereby saidmethod dynamically adapts said tutoring system, improves quality of saidtutoring process and accelerates learning success;
 4. a method as inclaim 3, wherein said making tutoring decisions from said plurality oftutoring decisions including making a plurality of diagnostic decisionseach revealing at least one cause of a fault behavior of said learner insaid learning activity, whereby said method enables focusing of thetutoring process on said cause of said fault behavior and correspondingacceleration of successful learning;
 5. a method as in claim 4, whereinsaid adapting said knowledge/data model by said processor includingrevising said knowledge/data model based upon a diagnostic decision fromsaid plurality of diagnosing decisions whereby said method focuses thetutoring process on said cause of said fault behavior of said learnerand accelerates successful learning;
 6. a method as in claim 3, whereinsaid making tutoring decisions from said plurality of tutoring decisionsby said decision maker including making a plurality of assignments fromsaid plurality of tutoring decisions to said media environment throughsaid media-logic converter to initiate respectively a plurality of extralearning activities of said learner, whereby said method realizes anactive manner of tutoring, eliminates prior manual sequencing oflearning activities by authors, improves quality of sequencing andaccelerates successful learning;
 7. a method as in claim 6, wherein saidmaking a plurality of assignments including a) making an assignment fromsaid plurality of assignments to supply progress of said learner; b)making an assignment from said plurality of assignments to test progressof said learner and detect at least one fault behavior of said learner;c) making an assignment from said plurality of assignments to diagnoseat least one cause of said fault behavior of said learner, whereby saidtutoring method dynamically realizes supply, testing and diagnosingmodes of active tutoring to accelerate learning progress;
 8. a method asin claim 6, wherein said making a plurality of assignments includingmaking a multiple assignment assigning a subset of the best learningactivities from said plurality of extra learning activities for finalchoice of one learning activity by said learner whereby said tutoringmethod supports mixed initiative learning/tutoring and accelerateslearning progress;
 9. a method as in claim 3, wherein said adaptingincluding improving said knowledge/data model including a) incrementingknowledge/data supported tutoring decisions justified by learningprocess; b) decrementing knowledge/data supported tutoring decisions notjustified by learning process; whereby said tutoring method improvesitself and accelerates learning progress;
 10. a system for tutoring alearner comprising a) a media environment for physical supporting atleast one learning activity of said learner, b) a unified logicgenerator for making a plurality of tutoring decisions; c) a media-logicconverter associated with said media environment and said logicgenerator for executing said tutoring decisions in said mediaenvironment and for providing said logic generator with at least onelearning report about said learning activity of said learner in saidmedia environment, wherein said logic generator monitors and controlsover said learning activity of said learner in said media environmentthrough said media-logic converter, whereby said system includesseparated media and logic components, provides unified logic-basedgenerating the specific media-dependent tutoring process, simplifiesauthoring, improves quality of said tutoring process and accelerateslearning success;
 11. a system for tutoring the learner as in claim 10,wherein said unified logic generator including a) a unifiedknowledge/data model referenced to said learner and said learningactivity including 1) a memory for storing knowledge/data, 2) a unifiedreusable framework for representing specific knowledge/data in saidmemory; 3) said specific knowledge/data about said learner and saidlearning activity filled in said unified reusable framework. b) aunified reusable tutoring engine including 1) a decision maker formaking a plurality of tutoring decisions based upon said unifiedknowledge/data model; 2) a processor for adapting and particularly forupdating said unified knowledge/data model based upon at least saidlearning report about at least said learning activity of at least saidlearner; wherein said unified logic generator obtains said learningreport about said learning activity of said learner in said mediaenvironment, adapts said unified knowledge/data model and makes saidplurality of tutoring, decisions to control over said learning activityof said learner, whereby said system provides unified reusablecomponents for easy building any specific tutoring systems simplifiesauthoring, improves quality of said tutoring process and accelerateslearning success,
 12. a system as in claim 11, wherein said unifiedreusable framework including a) a learning space framework forrepresenting a logical space of said learning activity; b) a learnerdata framework for representing said learner in said logical space;whereby said unified reusable framework specifies a priori unknowngeneric structure of said tutoring knowledge/data model;
 13. a system asin claim 12, wherein said learning space framework including at least a)a behavioral space framework for representing essential traceableaspects of said learning activity including at least one said report; b)a state space framework for representing untraceable aspects of saidlearning activity essential for making said plurality of tutoringdecisions, c) a state-behavior relation for associating said state spaceframework with said behavioral space framework; whereby said learningspace framework further specifies the generic structure of said tutoringknowledge/data and enables logical inference of untraceable aspects ofthe learning activity from traceable behavior;
 14. a system as in claim13, wherein said state space framework including a) a plurality oflearning objectives; b) a plurality of possible achievement states ofeach learning objective from said plurality of learning objectivesincluding at least 1) a no-achievement state, 2) a supplied achievementstate and 3) a demonstrated achievement state; wherein saidno-achievement state can transit into said supplied achievement stateand said supplied achievement state can transit into said demonstratedachievement state; whereby said state space model further specifies apriori unknown generic structure of said tutoring knowledge/data aboutsaid untraceable aspects of said learning activity essential for makingsaid plurality of tutoring decisions;
 15. a system as in claim 12,wherein said learner data framework representing at least a plurality ofbeliefs corresponding to each learning objective from said plurality oflearning objectives including at least a) a no-achievement beliefcorresponding to said no-achievement state, b) a supplied achievementbelief corresponding to said supplied achievements state, c)demonstrated achievement belief corresponding to said demonstratedachievement state, whereby said beliefs flexibly position said learnerinto said state space framework;
 16. a system as in claim 13, whereinsaid state-behavior relation for each learning objective from saidplurality of learning objectives including a) a local demonstratingbelief associating a specific behavior from said behavioral spaceframework with said demonstrated achievement state of said learningobjective; b) a local supplying belief associating said specificbehavior from said behavioral space framework with said suppliedachievement state of said learning objective; c) a local fault beliefassociating said specific behavior from said behavioral space frameworkwith said no-achievement state of said learning objective; whereby saidstate-behavior relation flexibly associates the expected cases oflearning behavior with the learning states enabling logical inference ofthe learning states of said learner from the reported behavior of saidlearner in said learning media environment;
 17. a system as in claim 11,wherein said decision maker including a strategic decision maker makingparticularly a plurality of diagnostic decisions each revealing at leastone cause of a reported fault behavior of said learner in said learningmedia environment, whereby said reusable tutoring engine enablesfocusing of the tutoring process on said cause of said fault behaviorand corresponding acceleration of successful learning;
 18. a system asin claim 17, wherein said processor including a reviser for revisingsaid knowledge/data model based upon the diagnostic decision from saidplurality of diagnosing decisions and focusing said logic generator onsaid cause of said fault behavior of said learner, whereby said reviserfocuses the whole tutoring system on said cause of said fault behaviorof said learner and accelerates successful learning;
 19. a system as inclaim 11, wherein said decision maker including a tactic decision makerfor making particularly a plurality of mode decisions including at leasta) a rule for setting up supply mode of tutoring; b) a rule for settingup testing mode of tutoring; c) a rule for setting up diagnosing mode oftutoring; whereby said uniform tutoring engine dynamically adapts themode of tutoring in order to accelerate successful learning;
 20. asystem as in claim 11, wherein said decision maker including anoperative decision maker for assigning at least one best learningactivity from said plurality of extra learning activities for thelearner in each mode from said supply, testing and diagnosing modes,whereby said logic generator eliminates prior manual sequencing of extralearning activities during authoring process, improve quality of saidsequencing and accelerates successful learning in the tutoring stage.